The driver’s impressions and feelings are crucial for L3AD driving since he/she should be able to resume vehicle control if needed. Therefore, they are taken into account in the whole development process of the different components that constitute the automated system. The analysis started with a traffic injury analysis. The research revealed considerable differences among countries in traffic road injuries, especially in relation with the gross-domestic-product (GDP) of the countries. The next step dealt with the analysis of user expectations. The aim was to explore what people think about automated vehicles and how comfortable they feel with this new technology. Therefore, a questionnaire was prepared and issued to volunteering participants. A broad overview of critical driving scenarios developed through the TrustVehicle project was compiled, where special focus was on the use cases represented by Ford Otosan’s truck/trailer backing manoeuvres, Tofaş’ LCV scenarios for narrow urban streets, Linkker’s charging station use case, Volvo’s sensor monitoring system, Valeo’s self-cleaning sensors and IFAT’s driver monitoring system. In addition, questionnaires and tests on the driver simulator were undertaken within TrustVehicle to assure this involvement of the user in the development process, either for planners and controllers, sensors and sensor monitoring or HMI. A modular co-simulation approach assured the flexibility needed within the development process.
Altogether TrustVehicle focused on 4 different vehicle classes (passenger car, bus, truck and light-commercial vehicle) within 6 different use cases.
Summarized the following results could be achieved:
• Driver Face Monitoring and Identification: Implementation of new time-of-flight camera-based sensor functionalities for driver monitoring and support
• Concept, Optimization and Testing of Sensors Cleaning Functions under Adverse Weather: Design of an AD system supervision for level 3 and 4 functions allowing to recover or maintain the sensors functionality.
• Sensor Monitoring and Self-Cleaning Sensors for a Passenger Vehicle: 3 developed methods for quantifying the availability of sensors during cleaning and in critical scenarios
• Heavy Truck-Trailer Reverse Parking Feature: Feature for completely autonomous smooth reverse parking manoeuvres demonstrated on an urban construction site and a truck docking station including driver HMI
• HMI for an Electric Bus Driving Automated under the Charging Station: Specially developed Bus Driver HMI to monitor when the electric bus is driven in automated driving mode safely and accurately under the charging station.
• Light Commercial Vehicle Sensor Fusion Development: Sensor fusion algorithms implemented for LIDAR, Camera and ultrasonic sensors for autonomous narrow street manoeuvring and reverse parking and flexible HW platform suitable for sensor fusion to ensure the correct functioning of HW peripherals and interfaces.
The main exploitable results within TrustVehicle can be divided into two categories:
• commercial exploitation (new or advanced technologies): ranging from new technologies and knowledge when it comes to human machine interaction and driver monitoring via new reliable sense-plan act approaches to new concepts for verification and assessment.
• scientific exploitation: new knowledge
o via publications
o for academic teaching
o to feed results into future research activities
o for know-how transfer to industrial partners
o for further joint research activities.
In addition, almost 50 dissemination activities have been performed throughout the lifetime of the project, ranging from papers, conference presentations to dissertations, with the final public event and the Springer book as highlights on top.
