Safe driver assistance systems through cognitive modeling and reactive synthesis

Research into safety in autonomous and semi-autonomous vehicles has, so far, largely been focused on testing and validation through simulation. Due to the fact that failure of these autonomous systems is potentially life-endangering, formal methods arise as a complementary approach. This project studies the application of formal methods to the verification of a human driver model built using the cognitive architecture, and to the design of correct-by-construction Advanced Driver Assistance Systems (ADAS).

The aim is to create a rigorous framework for the design of ADAS in semi-autonomous vehicles that accounts for the congestive process of the driver. To illustrate the validity of the approach, various driving scenarios will be studied, including multi-lane highway driving scenarios, in which interactions with other vehicles arise.

Related Publications

  1. F. Eiras and M. Lahijanian, “Towards Provably Correct Driver Assistance Systems through Stochastic Cognitive Modeling,” in Robotics: Science and Systems Workshop on Robust Autonomy: Tools for Safety in Real-World Uncertain Environments, 2019.

  2. M. Wu et al., “Gaze-based Intention Anticipation over Driving Manoeuvres in Semi-Autonomous Vehicles,” in Proceedings of the IEEE International Conference on Intelligent Robots and Systems, 2019, pp. 6210–6216.

  3. F. Eiras, M. Lahijanian, and M. Kwiatkowska, “Correct-by-Construction Advanced Driver Assistance Systems based on a Cognitive Architecture,” in 2019 IEEE 2nd Connected and Automated Vehicles Symposium, 2019, pp. 1–7.