Self-Adaptive Motion Prediction-Based Proactive Motion Planning for Autonomous Driving in Urban Environments

Abstract

This paper presents self-adaptive motion prediction-based proactive motion planning for autonomous driving in urban environments. In order to achieve fully autonomous driving in urban environments, the proposed algorithm predicts the future behavior of moving vehicles and considers the potential risk of objects appearing suddenly from occluded regions. A self-adaptive motion predictor was used to predict the probabilistic future states of vehicles and estimate the uncertainty of prediction simultaneously. Then, a free space boundary and drivable corridor were defined to determine the desired longitudinal acceleration and path. Based on these two boundary definitions, the proposed proactive motion planner decided the desired longitudinal motion by considering the risk potential of objects, such as the appearance of vulnerable road users from the free space boundary. The desired path is determined within the drivable corridor by the proposed motion planner with an integrated motion optimizer. The performance of the proposed algorithm has been validated via vehicle tests. The test results demonstrated that the proposed algorithm proactively determined lateral and longitudinal motion to minimize risks caused by detected and possible objects simultaneously on complex narrow roads to ensure safety.