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Research on driving behavior decision making system of autonomous driving vehicle based on benefit evaluation model

Wang Pengwei, Gao Song, Li Liang, Cheng Shuo, Zhao Hailan

Archives of Transport

2020

Vol. 53, iss. 1

21--36

Autonomous driving vehicle could increase driving efficiency, reduce traffic congestion and improve driving safety, it is considered as the solution of current traffic problems. Decision making systems for autonomous driving vehicles have significant effects on driving performance. The performance of decision making system is affected by its framework and decision making model. In real traffic scenarios, the driving condition of autonomous driving vehicle faced is random and time-varying, the performance of current decision making system is unable to meet the full scene autonomous driving requirements. For autonomous driving vehicle, the division between different driving behaviors needs clear boundary conditions. Typically, in lane change scenario, multiple reasonable driving behavior choices cause conflict of driving state. The fundamental cause of conflict lies in overlapping boundary conditions. To design a decision making system for autonomous driving vehicles, firstly, based on the decomposition of human driver operation process, five basic driving behavior modes are constructed, a driving behavior decision making framework for autonomous driving vehicle based on finite state machine is proposed. Then, to achieve lane change decision making for autonomous driving vehicle, lane change behavior characteristics of human driver lane change maneuver are analyzed and extracted. Based on the analysis, multiple attributes such as driving efficiency and safety are considered, all attributes benefits are quantified and the driving behavior benefit evaluation model is established. By evaluating the benefits of all alternative driving behaviors, the optimal driving behavior for current driving scenario is output. Finally, to verify the performances of the proposed decision making model, a series of real vehicle tests are implemented in different scenarios, the real time performance, effectiveness, and feasibility performance of the proposed method is accessed. The results show that the proposed driving behavior decision making model has good feasibility, real-time performance and multi-choice filtering performance in dynamic traffic scenarios.

The effects of food and drink intake to driving performance: a systematic review

Sutalaksana Iftikar Zahedi, Pratama Gradiyan Budi, Yamin Putra Alif Ramdhani, Soetisna Herman Rahadian

Transport Problems

2019

T. 14, z. 1

5--12

Human-related factors are considered to be the main cause of traffic incidents or accidents, causing 69.70% of the incidents. Several studies have been conducted to identify the relationship between drowsiness or fatigue and driving performance. Furthermore, a number of other studies not only discussed the symptoms causing drowsiness but also tried to investigate related factors that cause sleepiness or fatigue while driving. On the other hand, some discussed the quantity and quality of sleep as well as food and drink intake before and while driving. This systematic review, which is based on the PRISMA method, aims to map previous studies that investigated the effect of different food/drink consumption, either taken prior to driving or while driving, on the on-road driving characteristics of drivers. Furthermore, this article is expected to serve as a reference for further research that could potentially contribute to minimizing driving errors that lead to incident or accident. From 1871 articles screened, 7 studies related to food/drink intake and driving performance were reviewed. On the basis of the existing studies, no real evidence showing the presence of the association between food intake and the monotony of the road to decrease the driving performance has been found; therefore, further research is needed.