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Risk-aware Autonomous Driving Using POMDPs and Responsibility-Sensitive Safety

Examiner: Karl H. Johansson

Time: Mon 2021-06-07 14.00 - 15.00

Location: Zoom link:

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.  
Page responsible:Web editors at EECS
Belongs to: Decision and Control Systems
Last changed: Jun 01, 2021