Fine-Grained Driving Behavior Prediction via Context-Aware Multi-Task Inverse Reinforcement Learning
Kentaro Nishi, Masamichi Shimosaka
Abstract
Research on advanced driver assistance systems for reducing risks to vulnerable road users (VRUs) has recently gained popularity because the traffic accident reduction rate for VRUs is still small. Dealing with unexpected VRU movements on residential roads requires proficient acceleration and deceleration. Although fine-grained prediction of driving behavior through inverse reinforcement learning (IRL) has been reported with promising results in recent years, learning of a precise model fails when driving strategies vary with contextual factors, i.e., weather, time of day, road width, and traffic direction. In this work, we propose a novel multi-task IRL approach with a multilinear reward function to incorporate contextual information into the model. This approach can provide precise long-term prediction of fine-grained driving behavior while adjusting to context. Experimental results using actual driving data over 141 km with various contexts and roads confirm the success of this approach in terms of predicting defensive driving strategy even in unknown situations.