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Electronics 2019, 8(1), 40; https://doi.org/10.3390/electronics8010040

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

1
,
1,2,* , 1
,
1,* and 1
1
College of Intelligence Science, National University of Defense Technology, Changsha 410073, China
2
Unmanned Systems Research Center, National Innovation Institute of Defense Technology, Beijing 100091, China
*
Authors to whom correspondence should be addressed.
Received: 2 November 2018 / Revised: 17 December 2018 / Accepted: 20 December 2018 / Published: 1 January 2019
(This article belongs to the Section Systems & Control Engineering)
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Abstract

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. View Full-Text
Keywords: maneuver prediction; dynamic Bayesian network; intelligent vehicles; feature extraction maneuver prediction; dynamic Bayesian network; intelligent vehicles; feature extraction
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Li, J.; Dai, B.; Li, X.; Xu, X.; Liu, D. A Dynamic Bayesian Network for Vehicle Maneuver Prediction in Highway Driving Scenarios: Framework and Verification. Electronics 2019, 8, 40.

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