STMicroelectronics (ST) and Virscient are collaborating to enable faster delivery of connected-car systems with ST’s Telemaco3P automotive application processors. Virscient offers support to ST customers in the development and delivery of advanced automotive applications based on the ST Modular Telematics Platform (MTP).
STMicroelectronics is a global semiconductor leader serving customers across the spectrum of electronics applications. Virscient is a provider of hardware and software development services and support for customers building automotive solutions using ST’s Telemaco3P secure telematics and connectivity processors.
Virscient’s connected-car systems rely on technologies such as GNSS (precise positioning), LTE/cellular modems, V2X technologies, Wi‑Fi, Bluetooth and Bluetooth Low Energy.
MTP is a comprehensive development and demonstration platform incorporating ST’s Telemaco3P telematics and connectivity microprocessor. MTP enables the rapid prototyping and development of smart-driving applications, including vehicle connectivity to back-end servers, road infrastructure and other vehicles, the companies said.
The Telemaco3P incorporates Dual-Arm Cortex-A7 processors with an embedded hardware security module (HSM), an independent Arm Cortex-M3 subsystem, and a set of connectivity interfaces. With security at its core, and considerable flexibility in both hardware and software configurations, the Telemaco3P provides an excellent platform for connectivity within the vehicular environment.
“We chose to collaborate with Virscient for Telemaco3P-based designs because of their differentiated expertise in the development of embedded systems and wireless technologies, and their proven track record of helping customers take connected products from concept to market,” said Philippe Prats, head of EMEA marketing and application for STMicroelectronics’ automotive and discrete products. “The Telemaco3P platform enables our customers to deliver new categories and products in automotive telematics. By working with Virscient, we make this exciting technology accessible to a broader range of innovative companies.”
Commenting on the collaboration, Dr. Murray Pearson, CEO of Virscient, said, “We’re thrilled to work with STMicroelectronics to enable more companies to deliver innovative and market-leading platforms using the Telemaco3P devices.”
“ST and Telemaco3P are setting the security standard for processor and connectivity solutions in vehicular telematics. By leveraging Virscient’s hardware and software development capabilities, and our considerable experience with embedded wireless and connectivity technologies, Telemaco3P customers can push the envelope, and get their products to market quicker than ever.”
ST and Virscient are exhibiting the Modular Telematics Platform within the ST Automotive Telematics Ecosystem at Embedded World, ST stand (Hall 4A-138), Feb. 26-28, in Nuremberg, Germany.
The European GNSS Agency (GSA) is organizing a public consultation on the Integrity & Reliability of Digital Maps for Connected and Automated Driving, in connection with the recently published Commission Communication on Connected and Automated Mobility.
This communication addresses the need to investigate the integrity and reliability of digital maps in order to facilitate the deployment of fully automated and connected vehicles.
Image: GSA
Digital maps are an essential building block to ensure a safe driving experience for highly automated driving and autonomous vehicles. Purpose-built maps will be produced that will be much more reliable and accurate than those used for traditional applications.
These digital maps will be enriched with information from public databases and sensor data from connected vehicles. Traffic information, such as speed limits or the real-time dynamics of traffic flow, will help the vehicle’s navigation system to anticipate upcoming road conditions and take decisions beyond what is enabled by the vehicle’s on-board sensors.
Key role for GNSS
Satellite navigation (GNSS), and in particular Galileo, plays a key role in providing precise and secure positioning in vehicle navigation technologies for driverless mobility. Moreover, GNSS is the primary sensor for building digital maps to provide very accurate positioning together with other sensors, such as LiDAR, for example.
Dynamic data pose specific problems, particularly given their real-time nature: they must be generated, validated and made available to the user equipment without delay. This makes their integrity validation more challenging, and their transmission can be subject to errors or disruptions affecting the overall reliability.
Addressing the issue
Currently, it is the navigation and map provider’s responsibility to ensure the integrity of its products and the reliability of the information provided by third-party suppliers. However, until now the maps have been mainly used to support navigation, giving information to the driver, rather than to support safety-related functions.
Some industry standards exist or are being developed for data exchange and map content, but there are currently no specific standards or certification procedures to assess map data quality characteristics, such as reliability, integrity, and traceability. This public consultation is a starting point in addressing this issue.
Have your say
The public consultation can be accessed here. It will be open until Jan. 27.
“As of today [Nov. 28] Tesla owners have driven 1 billion miles with Autopilot engaged,” the company announced via tweet.
The Autopilot feature became available in 2015 and now comes on all new Tesla models with a $5,000 activation fee at the time of purchase or $7,000 if selected later.
The company is training its “neural networks” to improve its self-driving system.
Photo: Tesla
Tesla’s global fleet totals more than half a million vehicles, and recently marked a 20-billion mile step of total electric miles driven, the company said.
The Autopilot system can also function in the background of the vehicle, without being activated and with no input on control. Thus it gathers data from many more billions of “drivered” miles about its environment and potential Autopilot behavior.
The company previously mentioned the 1 billion-mile autonomous mark as the minimum it would need to move Autosteer from beta to a regular feature.
Updates to Autopilot are planned for 2019, including new hardware that will aid in the rollout of the company’s Full Self-Driving system, possibly by the end of that year.
Representatives from the global automotive industry gathered at the the Intelligent Transport Systems (ITS) World Congress in Copenhagen in September. At a “Galileo for Mobility” session, panelists showed off new products and discussed the benefits of GNSS for the deployment of multimodality, new mobility services and digital platforms by transport authorities, industries and users.
Their goal: to make safe driverless road transport a reality.
Autonomous driving with multi-GNSS
Cover image of Galileo for Mobility leaflet. (Image: GSA)
Germany’s ANavS GmbH provides position and attitude solutions with centimetre-level accuracy. Fast fixing is achieved by using three GNSS constellations and the company’s patented RTK fixing technology. The system combines multi-GNSS (GPS + GLONASS + Galileo), inertial sensors, vehicle data, visual odometry and feature mapping, as well as LiDAR and radar. Tight coupling of GNSS and all of these other systems ensure reliable positioning even in areas with limited satellite visibility.
ANavS managing director Patrick Henkel said, “Our sensor fusion framework delivers precise position and attitude information for navigation. It also generates real-time, highly accurate maps with high resolution. The platform can be used for the whole range of transport applications from road transport to maritime and drone navigation, as well as in robotics, surveying applications and of course in agriculture for precision farming.”
The system is particularly well suited to autonomous driving applications because of its high accuracy, high availability and continuity, and, with Galileo, its integrity, according to Henkel. The ANavS module is available in different versions, with one, two or three integrated GNSS receivers, depending on the level of performance required.
Sensor fusion with non-connected vehicles
Swedish truck manufacturer Scania led work on the EU-funded project, Precise and Robust Positioning for Automated Road Transports (PRoPART), demonstrating a high-availability positioning solution for connected automated driving applications. The system implements sensor fusion using information from both the on-board vehicle sensors and an off-board road infrastructure traffic sensor, accounting also for non-automated and non-connected road vehicles.
“We are benefiting from the high multipath mitigation enabled by the Galileo binary offset code, and there is a substantial improvement of reliability of the carrier phase ambiguity resolution,” said senior engineer Fredrik Hoxell. “All of this makes Galileo a really good addition to our sensor platform,” he said.
Big data contribution
Digital mapping is of course a critical resource for autonomous driving applications, and Tom Jensen of the veteran manufacturer of personal navigation devices TomTom stated “We have been compiling data from our GNSS receiver users for 10 years. We have 500 million devices currently running and today we have about 90 trillion data points!”
TomTom has dedicated itself to fusing that data for the generation of detailed maps that can be updated within minutes, for understanding traffic flow and traffic changes in near real time. “Now we want to open that up for the users,” he said. “We are meeting with public authorities, governments, decision makers who we know can use this information, for the roads, for the infrastructure, to plan their projects in the best and most intelligent way.”
Preventing terrorist attacks
The H2020-funded TransSec project coordinated by Daimler AG Trucks targets a solution to the recent rise in vehicle-based terror attacks across Europe, often employing heavy trucks to attack pedestrians.
Oihana Otaeguim, head of ITS at TransSec project partner Vicomtech, said, “We are developing and evaluating autonomous systems to detect and prevent trucks from being misused, to prevent these incidents from occurring. The trustability provided by Galileo is very remarkable. We have achieved advances in GNSS positioning, map data and map matching. On-board environment sensors and V2X communication are all combined in a local dynamic map. This can then be used for movement monitoring, critical area alarm, pre-crash object detection and for the implementation of non-defeatable emergency manoeuvres.”
The project team is also concerned with developing new and more effective methods to combat GNSS jamming and spoofing, which represent further threats to security in the context of automated driving technologies. Here, Galileo’s unique authentication feature will play an important role.
3D mapping
Japan’s Strategic Innovation Promotion Program, Automated Driving for Universal Services (SIP-adus) conducts several activities previewing the next generation of road transport systems: the human-machine interface in for autonomous and semi-autonomous driving, and the application of automated driving technologies in buses. The goal is precise stopping at bus stops with almost no space between the bus and the curb, to facilitate boarding and exiting for wheelchair users and elderly passengers.
“The project is validating the specifications and accuracy of a high-accuracy 3D mapping function,” Satoru Nakajo of the University of Tokyo said, “including data updating and distribution systems, and of the critical linkage of dynamic data delivered via road infrastructure.”
Public transport on demand. Area Metropolitana de Barcelona (AMB) will replace an existing fixed bus line with low demand with a flexible service that adapts bus routes according to the actual demand, improving the service and engaging new users without increasing public expenditure. The Galileo-based technology platform will consist of a mobile app and a system that manages requests, confirmations and cancellations, finds the best routes, and monitors distances travelled and payments.
Shared taxis. The pilot aims to alleviate Thessaloniki’s city centre congestion by reducing the number of trips from two eastern suburbs to the city. Ride sharing will be offered to commuters through 20 taxis provided by Taxiway at a flat rate.
Service aggregator. The Mobility as a Service (MaaS) app gathers mobility services available in Barcelona, Madrid and other big cities in Spain. It includes public transport, sharing services by motorbikes, bikes and cars, and bike parkings in these cities, improving accuracy and availability in urban areas, enabling a fast and smooth transition between transport modes, and offering the user a door-to-door and seamless multimodal trip experience.
Campus shuttle. The pilot will link autonomous electric vehicles to major hubs in a university or hospital campus (location to be determined).
Vehicle sharing. The Clem’ project will operate a last-mile transportation service to the community in Plateau de Saclay, an urban campus under development in the suburbs of Paris designed to welcome 85,000 students, workers and inhabitants by 2025. The pilot will include sharing a mixed fleet of 10 geolocated electric cars and 20 electric bikes.
This account drew heavily from published reports by the European GNSS Agency (GSA), available in full here.
New connected-car automotive microcontroller (MCU) enables secure remote updates and high-speed in-vehicle networking.
Image: STMicroelectronic
STMicroelectronics (ST) has launched a new flagship SPC58 H Line as part of its Chorus series of automotive microcontrollers (MCUs).
The new line can run multiple applications concurrently to allow more flexible and cost-effective vehicle electronics architectures.
The SPC58 H line has three high-performance processor cores, more than 1.2MB RAM and powerful on-chip peripherals, the company said.
As critical vehicle powertrain, body, chassis and infotainment features increasingly become defined by software, securely delivering updates such as fixes and option packs over the air (OTA) enhances cost efficiency and customer convenience, the company said.
With high security and large on-chip code storage, ST’s Chorus automotive microcontroller is a gateway/domain-controller chip capable of handling major OTA updates securely.
Two independent Ethernet ports provide high-speed connectivity between multiple Chorus chips throughout the vehicle and enable responsive in-vehicle diagnostics. Also featuring 16 CAN-FD and 24 LINFlex interfaces, Chorus can act as a gateway for multiple ECUs (electronic control units) and support smart-gateway functionality via 2 Ethernet interfaces also on-chip.
“The way carmakers create, configure, deploy and maintain new vehicles is changing, as software-defined functionality makes advanced features, flexibility and convenience ever more widely accessible,” said Luca Rodeschini, microcontroller business unit director at STMicroelectronics. “Our latest and highest-performing Chorus microcontroller, being OTA-ready and with dual Ethernet ports up to Gigabit speeds, creates a state-of-the-art platform for seamless, safe and secure in-car connectivity and control.”
To protect connected-car functionalities and allow OTA updates to be applied safely, the new Chorus chip contains a Hardware Security Module capable of asymmetric cryptography. Being EVITA Full compliant, it implements industry-leading attack prevention, detection and containment techniques.
Some customers have received samples of the SPC58 Chorus H Line microcontrollers in the next generation of smart gateways and central body modules, and are also evaluating the devices for battery-management units and ADAS safety controllers.
Cohda Wireless has successfully demonstrated its connected autonomous vehicle technology in a live trial on the streets of the city of Adelaide, Australia.
The trial proved the potential for connected self-driven vehicles to make streets safer and that Cohda’s technology is effective even in challenging urban canyons.
In an area covering two city blocks east of Adelaide’s Victoria Square, the demonstration replicated a scenario that is a daily occurrence on the streets of cities all over the world.
In the scenario, two vehicles approach a four-way intersection at right angles to each other. Car 2, driven by a human, fails to adhere to the red-light signal and approaches the intersection at speed, intending to “skip” the red light. Car 1, a connected autonomous vehicle, is approaching the intersection from another direction and intends to proceed through the intersection on the green light.
In a real-life scenario, there would be a risk of a collision as human drivers will invariably approach the intersection when the light is green, fully confident that all other road users will obey the traffic signals. In an instance where Car 2 disobeyed the traffic signal and Car 1 was unable to see the approaching danger, due to visibility being obstructed by buildings or other infrastructure, a collision would be especially likely.
But as Cohda Wireless’s Chief Technical Officer Professor Paul Alexander explained, if the vehicles were connected using Cohda’s V2X (Vehicle-To-Everything) technology, a potential collision situation would be detected and avoided well in advance of it actually happening.
“We demonstrated that when vehicles are connected to each other using our smart V2X technology, Car 1, the connected autonomous vehicle, would detect that Car 2 is approaching the red light at speed and is probably not going to stop. This allows the connected autonomous vehicle to pre-emptively identify and respond to the threat by slowing down and stopping.”
“Cohda’s V2X technology allows vehicles to ‘speak to each other’ to extend their perception horizon,” added Alexander.
“The technology provides the vehicle with an awareness of its environment and risk factors associated with it, consistently and accurately up to ten times per second, enabling it to make decisions that a human being would not be capable of making as the driver of the vehicle.”
Cohda’s Smart Cars Smart City initiative was funded by the South Australian Department of Transport and Infrastructure’s Future Mobility Lab Fund. In June this year, Cohda Wireless took ownership of two specially-modified vehicles from the U.S. that it is using in advanced trials of its V2X (Vehicle-To-Everything) technology.
The two Lincoln MKZ sedans were fitted with the ADAS (Advanced Driver Assistance Systems), ROS (Robot Operating System) various sensors including lidar, radar, cameras, GPS as well as in-vehicle compute platform and Cohda’s GNSS- independent positioning technology.
The fusion and cooperation of the various sensors and Cohda’s V2X technology augment the vehicles’ perception capability and make the autonomous vehicles features more practical, to include threat detection, the dangers associated with blind intersections and vulnerable road users, the company said.
“Our goal today was not only to demonstrate the efficacy of our technology in enabling self-driven vehicles to communicate with each other, but also to do so in a city environment where so-called ‘urban canyons’ significantly affect the ability of systems reliant on Global Navigation Satellite Systems (GNSS) to achieve accurate positioning,” Alexander said.
“The area in the city of Adelaide in which the trial was conducted was one such urban canyon where positioning through GNSS can be off by up to 40 meters, but with our V2X Locate technology positioning accuracy is improved to within a meter.”
Photo: Cohda Wireless
Cohda Wireless demonstrated the efficacy and accuracy of its V2X-Locate system in a 2017 trial in New York City where it repeatedly demonstrated sub-meter accuracy while driving along Sixth Avenue, which has the tallest buildings in the Big Apple. Comparably tested GPS-based systems were as much as tens of meters off-course, at times showing cars driving through buildings.
Cohda’s V2X technology underpins and complements other technology used by autonomous vehicles such as cameras, sensors, radars and lidars by enabling cooperative perception.
“The role of technology in making our roads safer is probably not generally understood but we hope that this demonstration has helped to prove that with the appropriate technology and infrastructure, connected self-driving vehicles are safer to have on our roads than vehicles controlled entirely by human beings,” added Alexander.
This announced version of Qualcomm Technologies’ precise positioning framework supports single-frequency GNSS utilizing real-time kinematic (RTK) technology based on the GNSS receiver built into Qualcomm Snapdragon LTE modems and Qianxun SI’s precise positioning technology — all integrated in an automotive-grade LTE module provided by Quectel.
Using Qualcomm 3D dead-reckoning technology, the precise-positioning framework will enable automakers with a comprehensive 3D navigation solution combining multi-constellation GNSS precise positioning, inertial measurement units and other sensors to support next-generation vehicle capabilities, the companies said.
Capabilities include high-performance connected navigation as well as LTE-V2X vehicle-to-everything communications (also referred to as C-V2X PC5 across the globe) for enhanced road safety, improved traffic efficiency and autonomous driving.
Qualcomm Technologies’ precise positioning framework is designed to facilitate open-sky positioning performance from up to 3 meters to less than 1 meter, supporting lane-level positioning and potentially achieving accurate locations from a centimeter to a few decimeters when combined with select third-party GNSS correction services.
This framework is also designed to support a safer and convenient automated driving experiences (level 2 and above), as well as LTE-V2X applications based on positioning, velocity and heading information. Integrated into telematics modules based on the Snapdragon LTE modems, the precise positioning framework supports a cost-effective solution for automakers already including cellular connectivity into their vehicles.
“The efforts with Qualcomm Technologies and Quectel not only assists automakers in addressing the cost and complexities of integrated precision positioning services, but it also aids in creating hardware and service standards for the industry to promote this capability as a public service in the field of connected cars,” said Jinpei Chen, CEO of Qianxun SI. “We look forward to working with Qualcomm Technologies and Quectel to help deliver a solution for higher accuracy and positioning, particularly in dense environments such as in China.”
“In efforts to meet the positioning service requirements of mainstream automakers and Tier 1 suppliers, we felt that working with technology leaders like Qualcomm Technologies and Qianxun SI would be the best to deliver an intelligent, cost-effective and high-quality telematics module,” said Penghe Qian, CEO of Quectel. “The AG35 is our newest generation of automotive-grade modules that enables 4G connectivity and lane level positioning simultaneously, allowing the adoption of LTE-V2X and HD Map technologies on a broad scale.”
“The automotive industry is becoming increasingly dependent on high performance positioning technologies to support connected navigation, safety services and vehicle autonomy,” said Nakul Duggal, vice president of product management, Qualcomm Technologies, Inc. “At Qualcomm Technologies, our proven positioning and system integration capabilities, along with Quectel and Qianxun SI’s solutions, can provide customers with cost-effective precise positioning solutions. We are pleased to be working with China’s leading technology companies like Quectel and Qianxun SI to advance next-generation automotive capabilities that will drive the automotive industry forward.”
By Sam Pullen, Stanford University; Jim Kilfeather, Jim Goddard, Tom Nowitzky, Brinda Shah, Wen Doong, David Kagan, and Kerry Greer, Globalstar. To be presented at ION-GNSS+ 2018.
Globalstar is developing a connected car program for continuous, worldwide service to vehicles via satellite and terrestrial communications links.
This combines PPP corrections provided globally by the second-generation Globalstar low-Earth orbit (LEO) constellation with local-area corrections via LTE cellular signals in urban areas for connectivity anytime, anywhere. Both signals are broadcast at 2.4 GHz and include pilot channels used for ranging, augmenting GNSS ranging and providing robustness against jamming and spoofing.
The program provides enhanced navigation via continuous augmentation of GNSS with data derived from ground-based reference networks for sub-meter accuracy and integrity bounds on navigation errors to probabilities as low as 10-9 per operation. When this is combined with other on-board sensors and data such as lidar, radar, optics and IMUs, it will be possible to operate autonomously under almost all conditions with a very high degree of safety.
The key is combined use of PPP corrections globally and local-area CDGNSS/RTK corrections in high-density urban regions where it is economically beneficial. Both sets of augmentations are made available to vehicles. The global approach on the left side of the figure is primary, given its near-worldwide coverage based on the LEO satellite network broadcasting corrections within its licensed communications spectrum at 2.4 GHz. The P/N-modulated pilot component of the Globalstar satellite signals will be used for ranging to augment GNSS and provide additional robustness to RF interference or spoofing at GNSS frequencies.
Automotive technology provider ERM Advanced Telematics has launched the StarLink Tracker with Wi-Fi, which integrates advanced vehicle tracking, driver behavior monitoring, theft prevention, Bluetooth, Wi-Fi and 4G cellular capabilities in a single device.
The company’s products have been installed in more than 1.5 million vehicles worldwide, the company said.
The StarLink Tracker with Wi-Fi is the first product under ERM’s new Wireless Connect strategy, which aims to use wireless technologies to provide its partners — vehicle fleet management companies, vehicle manufacturers and importers and car insurance companies — with a competitive edge.
The StarLink Tracker is a modular solution that is designed for installation both in vehicles on the production line and in the aftermarket, for vehicles that have left the production line. It turns any vehicle in which it is installed into a connected car.
The modularity of the product allows the addition of capabilities anytime through the use of add-on products provided by ERM or by a third party. This can be done on demand and without any need to replace the StarLink Tracker device, which keeps functioning as the central tracking and communications unit under any such solution.
The StarLink Tracker with Wi-Fi took about a year to develop, and ERM has already received its first orders to supply the product from customers in the United States, India and Australia.
The 120-gram tracker creates Wi-Fi hotspots in the vehicle for up to eight devices. It features a GPS/GLONASS/Galileo location module and an ability to navigate inside underground parking lots or in mines; a 4G cellular modem; internal antennas, emergency button support and built-in data logger.
Other capabilities are internal management of up to 500 driver IDs, remote immobilization, wireless connectivity to a wide range of additional ERM and third-party products and many other features.
As the core infrastructure for a Connected Car applications, the product can integrate to full range of the vehicle’s internet connectivity needs, which are provided by the use of the tracking unit’s SIM card without the need for any additional SIM card, the company said.
The StarLink Tracker with Wi-Fi and products that ERM Advanced Telematics will launch in the future under its Wireless Connect strategy, can be installed using the installer’s standard smartphone which communicates through Bluetooth connection in order to configure the product and perform any required adaptations. All this can be much faster compared to many other telematics devices and with much less hassle that might have arised due to the need to hook-up and hide wires.
The StarLink Tracker with Wi-Fi is also equipped with a microphone and loudspeaker to initiate and receive calls and dial emergency numbers. One application for this can be E-Call (Emergency Call), such as in the European Union or just as an Emergency Call application.
When pressing the location unit’s emergency button or immediately after an impact above a certain intensity, the unit will allows conversation between the vehicle’s occupants and the emergency center personnel, who can hear what is happening in the vehicle and identify events such as threats against the driver or accidents.
The product will also provide information about the driver’s behavior, including careless driving, accidents, off-road driving, acceleration during turns, speed violations and more, information that can be used by the manager to significantly improve fleet management capabilities, performance and can decrease operational expenses.
NXP Semiconductors N.V. has announced the next phase in its Smart City collaboration with Columbus, Ohio, the winner of the 2016 U.S. Department of Transportation’s $40 million Smart City Challenge.
NXP will contribute key technologies for smart and safe mobility to the Smart Columbus Experience Center.
Smart Center. On June 30, the City of Columbus celebrated the opening of its Smart Columbus Experience Center. The center allows visitors to see how new mobility options, such as connected, autonomous, shared and electric vehicles, will help make Columbus a more connected community.
Hands-on educational experiences and technology demonstrations aim to show visitors how technology and innovation in transportation can grow the local economy and create ladders of opportunity for central Ohio residents.
Visitors to the Smart Columbus Experience Center will learn how Vehicle to Everything (V2X) Technology allows cars to communicate with each other as well as with intelligent traffic infrastructure to keep mobility safe and efficient. (Image: NXP USA)
Cohda Wireless. As part of the Smart Columbus Experience Center initiative, NXP and Cohda Wireless will deploy a connected vehicle environment through the center’s electric vehicle test drive area so drivers can experience this future technology in person.
NXP has also donated an electric motorcycle with an accompanying drone that alerts the driver to dangers or delays ahead.
Key smart city technologies
As part of its commitment to Columbus, NXP will continue to contribute key mobility technologies to the Smart Columbus Experience Center, including:
NXP’s RoadLINK V2X technology allows cars to communicate with each other as well as with intelligent traffic infrastructures. The IEEE802.11p Dedicated Short Range Communications (DSRC) standard allows cars to securely connect to each other as well as to infrastructure. DSRC technology is the only ADAS sensor that can look around the corner and offers lowest latency in the communication.
Smart Card IC technology that enhances transportation for all citizens by supporting secure and convenient public transportation ticketing and payment systems, including contactless transit fare solutions.
Highly secure NXP Radio Frequency Identification (RFID) solutions designed to promote public safety and convenience. Smart City applications for this NXP technology include vehicle window stickers that enhance driver convenience and reduce municipal costs by eliminating the need for stop-and-pay stations in public parking spaces.
NXP eBike and Drone demo at the new Smart Columbus Experience Center shows how drones could send real-time video of a traffic incident to a city emergency vehicle. (Image: NXP USA)
Concept of Operations released
Smart Columbus, the smart city initiative from the City of Columbus, in July released the Concept of Operations for its Connected Vehicle Environment (CVE) pilot.
The Concept of Operations outlines in detail how the CVE pilot will be implemented over the next two years. The pilot will involve:
113 road side units (RSUs) that will be installed at intersections with stoplights
up to 1,800 on-board units (OBUs) that will be installed on participating private, emergency transit and freight vehicles, and
12 vehicle-to-vehicle or vehicle-to-infrastructure applications that will be deployed, according to the document.
Goals of the CVE pilot include improvements of:
vehicle operator safety
intersection safety
school zone safety
reliability of transit vehicle schedule adherence
emergency vehicle response times
traffic management capabilities.
Smart city demonstrations. Visitors to the Smart Columbus Experience Center can try out electric automobiles. A fleet of six electric vehicles will be on display and is available for test drives through a connected vehicle environment provided by NXP and Cohda Wireless.
Vehicles on display or available for test drives include:
a BMW i3 provided by BMW
a Chevrolet Bolt provided by Dave Gill Chevrolet
a Honda Clarity provided by Honda
a Mercedes-Benz GLE 55e provided by Daimler
a Nissan LEAF provided by Nissan North America
a Toyota Prius Prime provided by Toyota.
An electric motorcycle provided by NXP and a Ford Ojo electric scooter are also on display.
Lane errors in a three-lane road, giving lane determination (yellow triangle). (Photo: Pavel Vinnik/Shutterstock.com)
A lane-keeping system uses a sensor-fusion engine integrating GPS and an IMU with a two-stage map-matching algorithm. The system does not require explicit lane-level geo-referencing, saving massive storage required for lane-level spatial reference information, and reduces the computational complexity of the map-matching algorithm.
By Mohamed M. Atia, Carleton University and Allaa Hilal, Intelligent Mechatronics Systems
Lane determination is an important feature of advanced automotive navigation and guidance systems. It can be used in advanced driving assistance systems (ADAS), lane-departure warnings, and self-driving cars to perform lane-level, turn-by-turn guidance and control. It is also valuable information for telematics applications such as usage-based insurance. Lane-estimation systems have been dominated by vision and infrared sensors. Light detection and ranging (lidar) has also been used as a lane-determination technique. Those systems depend on visually recognizable features and landmarks that may not be available in some areas due to weather conditions or unstructured environments.
In addition, visual data processing may need specialized accelerators and parallel computing platforms to satisfy real-time constraints. To explore other alternatives, several research projects have started to investigate the feasibility of using low-cost global positioning and navigation technologies such as GPS, micro-electromechanical systems (MEMS) inertial measurement units (IMU) and geographical information systems (GIS) as an alternate lane-determination technology. However, most current systems have two main drawbacks: they use high-end RTK GPS, which suffers from coverage issues, and they use explicit lane geo-referencing, which leads to increased storage and processing.
Here we investigate the feasibility of using standard GPS fused with low-cost MEMS-IMU and a road network that includes lane information but not explicitly storing geo-referenced lane-level links.
The accuracy of Standard Positioning Service (SPS) GPS is within 3.351 meters (m) with a 95 percent confidence level. Figure 1 shows the results of standard single-point positioning test for a stationary receiver.
FIGURE 1. Standard GPS 2D position accuracy in a stationary test. (Figure: Mohamed M. Atia and Allaa Hilal)
The standard lane width in North America is approximately 3.6 m, requiring an unbiased precise positioning solution of much less than 1.8 m. If a safety margin of 50% is considered, unbiased precise positioning of less than 0.9 m is needed. Therefore, a standard SPS GPS technology may not be precise enough to accurately determine the vehicle’s lane. Advanced precise positioning technology like differential GPS (DGPS) can be used with high-resolution lane-level maps to achieve the lane determination.
However, these techniques may require additional cost/infrastructures and extra processing. To target a lower cost lane-determination system, this work suggests the fusion of measurements from a standard GPS, MEMS IMU and road-level network.
The work includes a sensor-fusion engine that is developed to integrate GPS and IMU using a loosely coupled extended Kalman filter (EKF). Then, a two-stage map-matching algorithm using a Hidden-Markov-Model (HMM) and a least-squares (LS) regression is developed.
The system does not require explicit lane-level geo-referencing; consequently, it saves massive storage required to save explicit lane-level spatial reference information, and it reduces the computational complexity of the HMM algorithm by reducing the number of road segments the HMM needs to decode. The overall system is illustrated in Figure 2.
FIGURE 2. Illustration of the proposed system. (Figure: Mohamed M. Atia and Allaa Hilal)
PROBLEM DEFINITION
A geometric illustration of the problem is shown in Figure 3. The road-network map is represented as a set of connected segments. Each road segment is defined by a straight line segment with a start position and end position. Curved roads are approximated by a sufficiently large number of straight line segments. Based on this notation and geometric illustration, the estimation problem that this article is addressing is the determination of the lane on which the vehicle is moving.
FIGURE 3. Illustration of the lane determination problem. (Figure: Mohamed M. Atia and Allaa Hilal)
Map-Matching with Hidden-Markov Model. The simplest map-matching method, point-to-curve-matching, is performed by searching for the nearest road segments within a threshold from the current vehicle’s position. The distance is calculated between the vehicle’s position and its projection on the map segment. However, this approach is sensitive to state estimation errors, and it fails at intersections, joins, branches or dense parallel roads. For example, Figure 4 shows a situation where biased GNSS position measurements exist, and the wrong map segment is selected because of the pure dependence on the distance metric only (for instance, D1 is less than D2).
FIGURE 4. Wrong map-segment selection in intersection. (Figure: Mohamed M. Atia and Allaa Hilal)
To avoid these errors and to improve map-matching accuracy, the matching criteria must include several constraints such as map topology (connectivity), vehicle dynamics, road geometry and legal direction of motions. In this work, to consider these constraints, we keep a recent portion of the vehicle motion history and use it in the matching criteria. This strategy is known as curve-to-curve matching.
To process a noisy stream of data, the HMM algorithm is used. A Markov model is a stochastic model that describes a sequence of states. The transition from one state to another can be modeled by a conditional transition probability.
If the states are not directly observable (hidden) but can be indirectly observed through a sequence of outputs, the process is called a Hidden Markov Process. The HMM in this case is characterized by the transition probability and an emission probability that represents the probability that a given state generates a certain observable.
Both transition probability and emission probability constitute the Bayesian network of HMM. A fundamental problem of HMM is that, given a sequence of outputs, what is the best sequence of states that explains the observed outputs? This problem is solved by selecting the sequence of states that maximize the HMM probability.
This estimation process, called decoding, is solved using the Viterbi algorithm. In the proposed system, the hidden states represent map links, and the observable outputs are the vehicle poses. To develop a robust map-matching framework, the vehicle pose history, roads geometry, and map topology constraints must be considered. Therefore, the emission and transition probabilities of an HMM are formulated such that they reflect all of these constraints. The Bayesian network of the HMM for our system is shown in Figure 5. The vehicle states (poses) is obtained from the INS/GNSS filter described shortly.
FIGURE 5. Hidden Markov model for vehicle’s state map-matching. (Figure: Mohamed M. Atia and Allaa Hilal)
In the proposed work, the length of the processed buffer of the vehicle’s state is determined based on the traveled distance. The aim is to accumulate a reasonable geometric knowledge about the trajectory segment that enables the HMM to accumulate enough geometric and topological constraints to be able to select the correct sequence of road segments in difficult intersections, joins and exit/entry roads.
EKF GNSS/INS SYSTEM
The navigation problem can be modeled as a dynamic system of states vector x(t) as follows:
(1) (2)
(Figures: Mohamed M. Atia and Allaa Hilal)
where f(.) is a nonlinear dynamic model, w(t) is a stochastic system noise vector, u(t) is a control signal vector that triggers the transition from current state to a future state, y(t) is external measurements vector (observables), h(.) is a nonlinear measurement model and v(t) is a stochastic measurement noise vector. Using first-order Taylor series approximation, (1) and (2) can be linearized as follows:
(3) (4)
(5)
(6)
(Figures: Mohamed M. Atia and Allaa Hilal)
A Kalman filter calculates an optimal estimation of provided that w(t) and v(t) are zero-mean Gaussian noise vectors with covariance matrices defined by:
(7)
(8)
and δx is the error vector with zero-mean and a covariance matrix P defined by:
(9)
Using zero-hold discretization where derivative is approximated by:
(10)
where T is the sampling time, equations involving HMM probability can be written in discrete form as follows:
(11)
(12)
The optimal estimation of the error vector, δxk, given measurements, yk, is calculated using two steps: prediction,
(13)
(14)
and update,
(15)
(16)
(17)
(Figures: Mohamed M. Atia and Allaa Hilal)
In INS/GNSS systems, the dynamic system state transition (x(t)) is triggered by IMU sensors (accelerometer and gyroscopes) while GNSS measurements are used as observables (y(t)). The observables update in our case is GNSS position and velocity. Therefore, the measurement error model is defined as follows:
(18)
where H is defined as follows:
(19)
Lane Estimation. When the road segments have been accurately selected based on the filtered vehicle’s pose, the projection of the vehicle’s positions on segment lanes can be easily calculated knowing the lane widths and number of lanes. The sum of squared errors for each lane is then calculated by:
(20)
where N is number of epochs, and pv is the projection of vehicle’s position on lane. The lane associated with the minimum error is selected as the designated lane.
(Figures: Mohamed M. Atia and Allaa Hilal)
Lane-Change Detection. If a lane change occurred within the processed buffer of data, the least-squares regression will not converge to the correct lane. Therefore, the buffer needs to be partitioned at the lane-switch locations. Thus, a lane-change detection module is developed. In this work, a lane-change detection method is designed based on capturing the patterns of the vehicle’s orientation and raw gyroscope measurements. The heading and raw gyroscope measurements during lane changes are shown in Figure 6 and Figure 7.
FIGURE 6. Vehicle’s heading during lane change to left. (Figure: Mohamed M. Atia and Allaa Hilal)FIGURE 7. Vehicle’s gyroscope measurements during lane change to left. (Figure: Mohamed M. Atia and Allaa Hilal)
The general pattern that the lane-change module detects is a peak or a valley in azimuth accompanied by a peak/valley or valley/peak sequence in the gyroscope measurements. To detect peaks and valleys, the standard deviation of a moving window of data is calculated and compared to a peak/valley threshold. If both gyro and azimuth peak/valley sequence are consistent and matched with the pattern described above, a lane change is declared.
Two algorithm phases of processing are then applied:
Acquisition Phase. GNSS and IMU measurements are fused in the main EKF, and HMM map-matching is performed and a lane is estimated. The innovation sequence of the main EKF, which is the difference between the predicted state and GNSS updates, is calculated over a buffer of data. If the innovation sequence is within a small threshold and no lane change has been detected, the acquisition phase is concluded and the tracking phase begins.
Tracking Phase. Two EKF filters are initiated. One EKF accepts position updates from the projection of the vehicle’s position on the selected lane, and the other EKF accepts GNSS position updates only. A discrepancy measure is evaluated between the two EKF instances for a short window of time. If this discrepancy measure is higher than a threshold, a temporary GNSS deviation is assumed and the system keeps reporting the current lane as the designated lane. If GNSS measurements started to be centered again on the new lane, a lane change is confirmed and the output of the first EKF instance will be the correct state. Otherwise, this lane change is declared as false and the second EKF output is the correct output. The overall block diagram of the proposed system is shown in Figure 8.
FIGURE 8. Overall block diagram of the proposed system. (Figure: Mohamed M. Atia and Allaa Hilal)
TESTS AND RESULTS
The proposed system has been tested on a computer connected to a GNSS receiver and an automotive MEMS-grade IMU, and road-network map data. A GPS-enabled camera was installed to capture video of the experiment, to be used as a ground truth to verify the results of our algorithms. Sensor specifications are given in Table 1 and Table 2. The effect of level arm (distance between IMU and GNSS antenna) was not considered in this implementation.
TABLE 1. GNSS receiver accuracy. (Table: Mohamed M. Atia and Allaa Hilal)TABLE 2. IMU specifications. (Table: Mohamed M. Atia and Allaa Hilal)
Three testing trajectories were collected during July 2015 through Highway 400 from Wilson Avenue in the south to Davis Drive in the north. Approximately 65 kilometers of trip data was collected. The data included some urban areas but was mostly open sky. It also included challenging road intersections and road joining/branching points. The experimental setup was designed such that the system automatically started when the vehicle’s engine was turned on. A Linux OS was installed on the gigabyte computer box, and a data acquisition firmware was configured to automatically begin when the computer starts. Measurements from the GNSS receiver at 1 Hz and the IMU at 50 Hz were synchronized on the computer. The main algorithm including GNSS/INS fusion and map-matching was developed in native ANSI C language for efficient processing. Original raw IMU data was set to 50 Hz down-sampled to 5 Hz. Within this interval, the real-time system could fetch map information from a cached database file, perform basic prediction steps and implement the forward calculation of a Viterbi algorithm (including calculation of emission and transition probabilities) that is needed for the HMM map-matching step.
Lane-Determination Results. The lane estimation results were logged and time-tagged. Using the video recording, the ground truth lane-level solution was visually inspected and manually recorded in a file. Since both the video camera and the proposed INS/GNSS/maps systems log data tagged by GPS time, synchronization between ground truth and the estimated lane were possible. The estimated lanes were visually inspected record by record and results were saved in an Excel sheet. The results were written into a time-tagged file where each row can be easily visually inspected by looking at the portion of images corresponding to the same time-tag. The time-tag used was the UTC-time contained in the NMEA GNSS raw measurements. The overall accuracy of the proposed system in lane determination is shown in Table 3.
TABLE 3. Lane-estimation accuracy. (Table: Mohamed M. Atia and Allaa Hilal)
Figure 9 and Figure 10 show example snapshots of the visual inspection software tool developed to evaluate the accuracy of the system. As can be seen in the figures, an image of the road that indicates the correct lane is displayed in the upper graph, while the estimated lane information is displayed along with road information including lane errors in the lower graph. Figure 10 shows that the system can identify the correct lane when the number of lanes is increased.
FIGURE 9. Lane errors in a three-lane road. (Figure: Mohamed M. Atia and Allaa Hilal)FIGURE 10. Lane errors in a four-lane road. (Figure: Mohamed M. Atia and Allaa Hilal)
CONCLUSION
This work described a low-cost lane-level positioning system using a conventional GNSS receiver, MEMS IMUand commercially available road-level network without the need for explicit spatial storage of lanes. The research used a conventional GNSS receiver and MEMS IMU with a computationally efficient two-stage HMM-based map-matching algorithm that avoids the explicit use of lanes as hidden states, which significantly reduces the size of the HMM network and consequently enhance its real-time performance. The proposed system provides an alternative lane determination method without the need for computationally expensive vision/lidar methods that may fail in dark, foggy or dynamically changing environments. The work showed extensive experiments under different road sections, showing an average lane-determination accuracy of 97.14%.
ACKNOWLEDGMENTS
This work was first presented at ION International Technical Meeting, January 2018.
MANUFACTURERS
The system comprises an Intel Celeron N2807 1.58-GHz Mini PC connected to a u-blox EVK-7P kit GNSS receiver and an automotive MEMS-grade IMU 3D space sensor IMU from YOST Labs, and road-network map data from HERE. A GPS-enabled HP f310 car camcorder captured video.
MOHAMED M. ATIA received a Ph.D. in electrical and computer engineering from Queen’s University at Kingston. He is assistant professor and founder/director of the Embedded Multi-sensor Systems research laboratory in Carleton University, Ontario, Canada.
ALLAA HILAL received a Ph.D. degree in electrical and computer Engineering from the University of Waterloo. She is director of the innovation and emerging technology department at Intelligent Mechatronic Systems, a connected-car company based in Waterloo, Canada.
Australian company Cohda Wireless has released a vehicle positioning system to eliminate GPS black spots in “urban canyons” between high-rise buildings.
Using Cohda’s expertise in developing collision avoidance systems for mines, the vehicle-based system, V2X-Locate, can identify vehicle position to sub-meter accuracy in environments that degrade GPS accuracy, such as tunnels, underground carparks and between high-rise buildings.
As well as enhancing current connected vehicles, V2X-Locate delivers a critical component for connected autonomous vehicles (CAV), which will require uninterrupted positioning data to safely navigate on roads, the company said.
Image: Cohda Wireless
Cohda has designed V2X-Locate to enable equipped vehicles to identify their location using existing Smart City V2X (vehicle-to-everything) roadside infrastructure from any standards-based manufacturer.
Cohda Wireless Chief Technology Officer Paul Alexander said V2X-Locate was a globally unique product. “We solve the problem caused by GPS and satellite-based positioning systems not working in all use-cases,” he said.
“If you’re in a major downtown area with tall buildings, or in a tunnel or in an underground parking lot, a GPS system can fail, preventing it from delivering accurate results,” Alexander said. “As well as being inconvenient for current drivers, this is not an option as we enter the era of driverless cars. The V2X-Locate breakthrough is to position the vehicle with sub-meter accuracy by using the existing communications signals produced by V2X Smart City infrastructure deployments. The result is that V2X-Locate can eliminate positioning black spots in city centers where they are most likely to occur.”
Cohda Wireless demonstrated V2X-Locate in a 2017 trial in New York City, where it repeatedly demonstrated sub-meter accuracy while driving along Sixth Avenue, which has the tallest buildings in the Big Apple. Comparably tested GPS-based systems were as much as tens of meters off-course, at times showing cars driving through buildings.
Alexander said Cohda Wireless had designed V2X-Locate by using its experience developing collision avoidance technology for underground mines. “The hardest place to do positioning is one kilometer underground with a cubic kilometer of copper above your head,” he said.
“That’s where V2X-Locate was born,” Alexander said. “Cohda has worked in that area for several years, providing accurate positioning for vehicles where no GPS connectivity is available. We’ve now successfully migrated that technology from mine sites of the outback to the urban canyons of New York City.”
V2X_Locate uses the NXP SAF5400 single-chip modem for V2X. (Photo: NXP)
Both Cohda’s standard V2X onboard units and roadside units use the NXP RoadLINK chipset, which supports V2X-Locate’s highly accurate performance by delivering multipath channel tracking.
After a pre-release international roadshow in October last year, Cohda Wireless received strong interest in V2X-Locate from both Smart Cities and Tier 1 automotive manufacturers. To meet that demand, Cohda Wireless has released a V2X-Locate Evaluation Kit, which contains the system and four roadside unit devices, which equip prospective customers to put V2X-Locate through its paces.
