Global Typical Projects of Connected Autonomous Driving

(7) MAVEN

The goal of MAVEN (Managed Autonomous Vehicle Augmented Network) is to provide C-ITS-assisted solutions for cooperative autonomous vehicles managing signalized intersections and intersection corridors to improve traffic efficiency and safety. MAVEN develops infrastructure-assisted platooning and negotiation algorithms. These facilitate autonomous vehicle management at signalized intersections and corridors. Thereby, vehicle systems for trajectory and maneuver planning and infrastructure systems for adaptive traffic light optimization are extended and connected. In MAVEN, traffic lights that adjust signal timing are studied. These traffic lights facilitate organized platooning of vehicles and enable better use of infrastructure capacity. Vehicle delays and emissions are thereby reduced. MAVEN develops a prototype for field testing and extensive modeling for impact assessment. It contributes to the development of enablers such as standards and high-precision maps. MAVEN also provides ADAS technologies for vulnerable traffic users (VRUs). MAVEN's goal is to develop a roadmap for the introduction of vehicle road automation to support road authorities in their tasks of changing their role and traffic management systems.

(8) MECView

Automated driving in complex urban environments is limited due to occlusions by relevant road users or obstacles – in these situations, the performance of onboard sensor systems is in principle limited, which cannot be compensated by onboard connectivity in the case of incomplete sensing capabilities or incomplete connectivity across the fleet. To address this problem, the publicly funded project MEC-View focuses on evaluating roadside sensor systems and high-precision digital maps of the driving environment that complement the sensor systems and processing capabilities of autonomous vehicles. Based on roadside sensor objects, a mobile edge computing (MEC) server frontend provides a local environment model to the autonomous vehicle via a 5G mobile network prototype. The entire system was implemented and validated in unrestricted urban traffic at a test area in the city of Ulm with a dedicated use case: an automated vehicle relies on a local MEC environment model to seamlessly enter a priority road at an urban junction. To meet these requirements, new methods for predicting dynamic objects and intention planning with the help of machine learning concepts are essential. The MEC-View project is committed to enabling safe and efficient automated driving in complex and challenging urban environments. In addition, the system improves the perception of vulnerable road participants, such as pedestrians, cyclists and motorcyclists.

(9) SecForCARs

SecForCARs is a collaborative project funded by the German Federal Ministry of Education and Research (BMBF) and consists of partners from the automotive industry, medium-sized companies and research institutes. Within the framework of the project, the partners are jointly investigating issues in information security and automated driving. As with all information processing systems, security in the vehicle sector cannot be neglected. Particularly in the context of driver assistance systems and, in the future, of automated driving, interventions in the driving control system create an interdependency between cybersecurity and safety. Within the framework of SecForCARs, the partners are jointly investigating weaknesses and vulnerabilities in modern cars. To this end, they are developing a security architecture as well as tools and test methods to integrate safety and cybersecurity into future development processes. In addition, security mechanisms are developed based on vulnerability assessments to detect and prevent attacks inside and outside the vehicle.

(10) Trans AI D

As the introduction of autonomous vehicles becomes feasible, it is necessary to investigate their impact on traffic safety and efficiency. This is particularly important in the early stages of market introduction, when autonomous vehicles of all SAE levels, connected vehicles (capable of communicating via V2X) and conventional vehicles share the same roads, with varying penetration rates. There will be areas and situations on the road where a high degree of automation can be achieved, while other areas and situations do not allow or are impossible due to loss of sensor input, high complexity, etc. In these areas, many automated vehicles will change their level of automation. TransAID refers to these areas as “transition zones”. The project develops and demonstrates traffic management procedures and protocols to enable the smooth coexistence of autonomous, connected and conventional vehicles, especially in transition zones. Following a layered approach, control operations are performed at different layers, including centralized traffic management, infrastructure and vehicles. TransAID defines traffic management procedures assisted by cooperative intelligent transportation systems (C-ITS) to mitigate the negative impact of transitions of control (ToCs) of autonomous vehicles along critical areas (e.g., road works, bottlenecks, highway merges, etc.) in future mixed traffic scenarios where autonomous, cooperative, and conventional vehicles will coexist. In this case, the C-ITS road infrastructure uses V2X to notify warnings (presence of non-autonomous driving areas) and recommended actions (preventive transitions of control or lane changes, etc.). When implemented by said CAV, these recommendations address traffic situations associated with possible ToCs.

2. Typical CAV Projects in North America: Canada

This section details several examples of pilot deployments and research institutions in Canada.

(1) Area XO

Founded and operated by Invest Ottawa, Area XO enables and accelerates the secure development, testing and application of next-generation technologies in the telecommunications industry, smart agriculture, defence, security and public safety, drones , and smart mobility, autonomous driving and connectivity for smart cities. The 1,866-acre facility provides: 1) V2X (vehicle-to-everything) testing, validation and demonstration in a four-season climate with temperatures ranging from −39 to +39 degrees Celsius (−38 to +102 degrees Fahrenheit). 2) Comprehensive test facilities with GPS, terrestrial wireless systems and satellite radio communication systems; network infrastructure; cybersecurity solutions; and industry-leading data collection, analysis and cloud capabilities. In the CAV and smart mobility space, Area XO is enabling innovations in: 1) Vehicle-to-Vehicle (V2V) radio communications use cases and systems 2) Vehicle-to-Infrastructure (V2I) technologies and systems 3) Next-generation networks to support V2V and V2I innovations and use cases 4) Software , hardware , and related cybersecurity technologies required to integrate these capabilities into autonomous vehicles and municipal infrastructure 5) CAV operations in inclement weather, including systems to pinpoint the location of hidden objects, cybersecurity, interoperability, and the use of CAV-generated data For more information, visit www.AreaXO.com and www.investottawa.ca

(2) Alberta Collaborative Transportation Infrastructure and Vehicle Environment (ACTIVE)

Launched in 2014, the Alberta Collaborative Transportation Infrastructure and Vehicle Environment (ACTIVE) testbed is a collaboration between the Government of Canada, the Government of Alberta, the City of Edmonton, the Centre for Smart Transportation (CST) at the University of Alberta, and the University of British Columbia. It is part of Canada's first network of connected vehicle testbeds. The testbed is managed by the University of Alberta and includes 51 roadside equipment (RSEs) deployed in the City of Edmonton, and an additional 15 RSEs deployed on private roads at the University of Alberta's South Campus. Two additional C-V2X RSEs are installed at the South Campus, next to the DSRC RSEs. Many of the connected intersections also include microwave or radar sensors for vehicle classification, or traffic cameras with remote video streaming capabilities. The ACTIVE testbed also contains a number of vehicles equipped with OBEs. The testbed is focused on studying how connected technologies can improve safety and traffic capacity. Demonstrated use cases included MAP and SPaT messages at connected intersections, pedestrian alerts, demonstration of trusted vs. untrusted messages (secured using credentials issued by a secure credential management system), red light and speeding warnings, and various standardized use cases using messages defined in SAE J2735. In addition, the test rig’s location makes it an ideal environment for testing CV systems in harsh winter conditions. For more information on this test rig, visit https://www.ualberta.ca/engineering/research/groups/smart-transportation/research/projects/connected-vehicles.html

(3) Automotive Testbed for Reconfigurable and Optimized Radio Access (AURORA)

The Automotive Testbed for Reconfigurable and Optimized Radio Access (AURORA) was launched in 2014 as a collaboration between the Government of Canada, the Government of Alberta, the City of Edmonton, the Centre for Smart Transportation (CST) at the University of Alberta, and the University of British Columbia. The testbed is managed by the University of British Columbia (UBC) and is part of the first network of connected vehicle testbeds in Canada. The current deployment consists of 3 connected intersections, each with a traffic camera, roadside unit, Wi-Fi access point for backhaul connectivity and a connection to a traffic signal controller . Vehicles equipped with OBEs can be used to send and receive messages (simulating BSM and SPaT messages) and other devices are being bench tested in a lab environment. The AURORA testbed focuses primarily on the physical radio communication layer and has produced results that allow for the detection of interference or misuse of the DSRC spectrum. For more information on this testbed, visit http://rsl.ece.ubc.ca/Aurora.html