Abstract
Self-driving safety is one of the major concerns raised with regard to pushing the use of automated vehicles on roads. Fully automated vehicles are forced to make appropriate decisions in an uncertain environment where driverless and human-driven vehicles share the road together. This study proposes a new model that can be used to enhance the autonomous driving behavior at a non-signalized intersection considering the traffic safety guarantee, delay time, and smooth driving. The proposed model is called the responsibility-sensitive safety-based partially observable Markov decision process model for the decision-making mechanism of automated vehicles. The model not only increases traffic safety guarantee and smooth driving, but also reduces the delay time. First, we generate some specific driving scenarios using the automated driving toolbox in MATLAB. Second, the driving strategy of automated vehicles is optimized by the partially observable Markov decision process framework using the adaptive cruise control system. Finally, the responsibility-sensitive safety algorithm is implemented under adaptive model predictive control. The proposed model performs better than the classical adaptive model predictive control. In the best case, the proposed model took a 31.60 % reduction in braking time and a 51.20 % improvement in smoothing speed control.
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This work was supported by a Research Grant of Pukyong National University (2019).
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Tran, D.Q., Bae, SH. Improved Responsibility-Sensitive Safety Algorithm through a Partially Observable Markov Decision Process Framework for Automated Driving Behavior at Non-Signalized Intersection. Int.J Automot. Technol. 22, 301–314 (2021). https://doi.org/10.1007/s12239-021-0029-z
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DOI: https://doi.org/10.1007/s12239-021-0029-z