Situational Assessments Based on Uncertainty-Risk Awareness in Complex Traffic Scenarios

Guotao Xie, Xinyu Zhang, Hongbo Gao, Lijun Qian, Jianqiang Wang and Umit Ozguner
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Guotao Xie: Department of Automotive Engineering, Hefei University of Technology, Hefei 230009, China
Xinyu Zhang: Information Technology Center, Tsinghua University, Beijing 100084, China
Hongbo Gao: State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China
Lijun Qian: Department of Automotive Engineering, Hefei University of Technology, Hefei 230009, China
Jianqiang Wang: State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China
Umit Ozguner: Department of Electrical and Computer Engineering, the Ohio State University, Columbus, OH 43210, USA

Sustainability, 2017, vol. 9, issue 9, 1-17

Abstract: Situational assessment (SA) is one of the key parts for the application of intelligent alternative-energy vehicles (IAVs) in the sustainable transportation. It helps IAVs understand and comprehend traffic environments better. In SA, it is crucial to be aware of uncertainty-risks, such as sensor failure or communication loss. The objective of this study is to assess traffic situations considering uncertainty-risks, including environment predicting uncertainty. According to the stochastic environment model, collision probabilities between multiple vehicles are estimated based on integrated trajectory prediction under uncertainty, which combines the physics- and maneuver-based trajectory prediction models for accurate prediction results in the long term. The SA method considers the probabilities of collision at different predicting points, the masses, and relative speeds between the possible colliding objects. In addition, risks beyond the prediction horizon are considered with the proposition of infinite risk assessments (IRAs). This method is applied and proved to assess risks regarding unexpected obstacles in traffic, sensor failure or communication loss, and imperfect detections with different sensing accuracies of the environment. The results indicate that the SA method could evaluate traffic risks under uncertainty in the dynamic traffic environment. This could help IAVs’ plan motion trajectories and make high-level decisions in uncertain environments.

Keywords: intelligent alternative-energy vehicles; situational assessments; uncertainty-risk awareness; infinite risk assessments (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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