[Paper] Smooth and Stopping Interval Aware Driving Behavior Prediction at Un-signalized Intersection with Inverse Reinforcement Learning on Sequential MDPs was accepted in IV2021

The 2021 IEEE Intelligent Vehicles Symposium (IV21) is a premier annual technical forum sponsored by the IEEE Intelligent Transportation Systems Society (ITSS), which will be held during 11-17 July 2021 online.

The following contents will be presented.

Driving behavior modeling (DBM) is widely used in the intelligent vehicle field to prevent accidents, which predicts actions that vehicles should take to optimize safe driving behaviors. According to some statistics, accidents easily happen at un-signalized intersections. Modeling driving behavior at such places is of great importance. However, current inverse reinforcement learning-based DBM methods fail to predict proper behaviors at the un-signalized intersections in the aspects of smoothness and stopping behavior by just using a single Markov decision process (MDP). We propose a novel sequential MDPs approach to model the driving behavior at the un-signalized intersections to solve the problems. Our approach decomposes the target behavior through the un-signalized intersections into three parts and models each decomposition’s driving behaviors with appropriate time durations by a stopping-time-interval distribution through dynamic programming. Experiments on real driving data show that the proposed method achieved a better result and successfully improved the smoothness and stopping awareness of the planned driving path compared to the baselines.

-presentation information- On-demand pre-recorded video available during July 11 09:00 – July 17 23:00 (JST)

Shaoyu Yang*, Hiroshi Yoshitake**, Motoki Shino**, Masamichi Shimosaka*: Smooth and Stopping Interval Aware Driving Behavior Prediction at Un-signalized Intersection with Inverse Reinforcement Learning on Sequential MDPs (*Tokyo Institute of Technology, **The University of Tokyo)