Risk-aware Autonomous Driving Using POMDPs and Responsibility-Sensitive Safety
Examiner: Karl H. Johansson
Time: Mon 2021-06-07 14.00 - 15.00
Doctoral student: Caroline Skoglund , DCS (Reglerteknink)
Opponent: Fabian Waldner
Supervisor: Truls Nyberg and Yulong Gao
Abstract: Autonomous vehicles promise to play an important role aiming at increased efficiency and safety in road transportation. Although we have seen several examples of autonomous vehicles out on the road over the past years, how to ensure the safety of autonomous vehicle in the uncertain and dynamic environment is still a challenging problem. This thesis studies this problem by developing a risk-aware decision making framework. The system that integrates the dynamics of an autonomous vehicle and the uncertain environment is modelled as a Partially Observable Markov Decision Process (POMDP). A risk measure is proposed based on the Responsibility-Sensitive Safety (RSS) distance, which quantifying the minimum distance to other vehicles for ensuring safety. This risk measure is incorporated into the reward function of POMDP for achieving a risk-aware decision making. The proposed risk-aware POMDP framework is evaluated in two case studies. In a single-lane car following scenario, it is shown that the ego vehicle is able to successfully avoid a collision in an emergency event where a vehicle in front of it makes a full stop. In the merge scenario, the ego vehicle successfully enters the main road from a ramp with a satisfactory distance to other vehicles. As a conclusion, the risk-aware POMDP framework is able to realize a trade-off between safety and usability by keeping a reasonable distance and adapting to other vehicles behaviours.