A Dynamic Bayesian Network for Vehicle Maneuver Prediction in Highway Driving Scenarios: Framework and Verification

Electronics

Xiaohui Li¹  Daxue Liu¹  Junxiang Li¹  Bin Dai¹  Xin Xu¹ 

[1] College of Intelligence Science, National University of Defense Technology, Changsha 410073, China;

关键词: maneuver prediction;    dynamic Bayesian network;    intelligent vehicles;    feature extraction;   

DOI  :  10.3390/electronics8010040

来源: DOAJ

【 摘 要 】

Accurate maneuver prediction for surrounding vehicles enables intelligent vehicles to make safe and socially compliant decisions in advance, thus improving the safety and comfort of the driving. The main contribution of this paper is proposing a practical, high-performance, and low-cost maneuver-prediction approach for intelligent vehicles. Our approach is based on a dynamic Bayesian network, which exploits multiple predictive features, namely, historical states of predicting vehicles, road structures, as well as traffic interactions for inferring the probability of each maneuver. The paper also presents algorithms of feature extraction for the network. Our approach is verified on real traffic data in large-scale publicly available datasets. The results show that our approach can recognize the lane-change maneuvers with an F1 score of 80% and an advanced prediction time of 3.75 s, which greatly improves the performance on prediction compared to other baseline approaches.

【 授权许可】

Unknown