Journal
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume 23, Issue 7, Pages 8477-8487Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2021.3082944
Keywords
Traffic safety; dangerous lane-changing probability; collision avoidance; mixed connected vehicle; hidden Markov model; back propagation neural network
Categories
Funding
- National Key Research and Development Program of China [2018YFB1600500]
- Chinese National Natural Science Foundation [U1564201, 51875255, 61601203]
- Jiangsu Provincial Six Talent Peaks [DZXX048, 2018-TD-GDZB-022]
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This paper presents a new control model for automatic vehicles to avoid dangerous lane-changing behavior, which combines probability models and neural network models to achieve collision avoidance control, and verifies the accuracy and effectiveness of the model.
This paper proposes a new avoidance control model for automatic vehicle in facing dangerous lane-changing behavior. Firstly, the new lane-changing probability factor based on Gaussian-mixture-based hidden Markov model is constructed to predict the lateral-vehicle lane-changing probability and output the pre-control parameters. Secondly, the back propagation neural network avoidance model, which combined with driver's avoidance behavior, is developed for achieving the instantaneous collision avoidance control. Moreover, the optimal solution between control parameters and vehicle stability is obtained by using linear quadratic regulator. Finally, the accuracy of the avoidance model is verified by the semi-physical driver-in-the-loop simulation based on PreScan/Simulink. Results show that the automatic vehicle with the proposed avoidance model can accurately and effectively take pre-braking and micro-steering behavior. The proposed model can greatly reduce vehicle collision probability and effectively take both safety and comfort of collision avoidance into account. In addition, the robustness of the control model under different network penetration is discussed.
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