Microscopic modeling of cyclists interactions with pedestrians in shared spaces: a Gaussian process inverse reinforcement learning approach

This study presents a microsimulation-oriented framework for modeling cyclists' interactions with pedestrians in shared spaces. The objectives of this study are to 1) infer how cyclists in head-on and crossing interactions rationally assess and make guidance decisions of acceleration and yaw rate, and 2) use advanced Artificial Intelligent (AI) techniques to model road-user interactions. The Markov Decision Process modeling framework is used to account for road-user rationality and intelligence. Road user trajectories from three shared spaces in North America are extracted by means of computer-vision algorithms. Inverse Reinforcement Learning (IRL) algorithms are utilized to recover continuous linear and nonlinear Gaussian-Process (GP) reward-functions (RFs). Deep Reinforcement Learning is used to estimate optimal cyclist policies. Results demonstrated that the GP-RF captures the more complex interaction behaviour and accounts for road-user heterogeneity. The GP-RF led to more consistent inferences of road-users behaviour and accurate predictions of their trajectories compared with the linear RF.