Risk Assessment and Mitigation in Local Path Planning for Autonomous Vehicles With LSTM Based Predictive Model

Accurate trajectory prediction of surrounding vehicles enables lower risk path planning in advance for autonomous vehicles, thus promising the safety of automated driving. A low-risk and high-efficiency path planning approach is proposed for autonomous driving based on the high-performance and practical trajectory prediction method. A long short-term memory (LSTM) network is trained and tested using the highD dataset, and the validated LSTM is used to predict the trajectories of surrounding vehicles combining the information extracted from vehicle-to-vehicle (V2V) technology. A risk assessment and mitigation-based local path planning algorithm is proposed according to the information of predicted trajectories of surrounding vehicles. Two driving scenarios are extracted and reconstructed from the highD dataset for validation and evaluation, i.e., an active lane-change scenario and a longitudinal collision-avoidance scenario. The results illustrate that the risk is mitigated and the driving efficiency is improved with the proposed path planning algorithm comparing to the constant-velocity prediction and the prediction method of the nonlinear input–output (NIO) network, especially when the velocity and trajectory with sudden changes. Note to Practitioners—This article was motivated by the problem of promising the safety decision-making and path planning through accurate environment prediction. There are two main parts included in this article. First, this article proposed one pragmatic approach to predict the environment movement correctly based on the long short-term memory (LSTM) approach. The prediction performance of LSTM was compared with nonlinear input–output (NIO). The results showed that the LSTM approach has a significant advantage in motivation prediction of the surrounded vehicles during path planning. The second part of this article is to make the decision and realize local path planning based on the risk assessment. The potential field-based approach is implemented on the risk assessment based on these accurate predictions. Some primary results demonstrate that the decision-making algorithm performs better under the accurate prediction model. The results also show that the safety and driving efficiency of the ego vehicle were improved by tracking the trajectory, which was planned based on the risk assessment. The only concern for the real-time application is the computation time; in future, we will figure it out how to further reduce the computation time.