Next Article in Journal
Supply Chain Configurations in the Circular Economy: A Systematic Literature Review
Next Article in Special Issue
Heuristic Optimization for the Energy Management and Race Strategy of a Solar Car
Previous Article in Journal
Impact of Firms’ Cooperative Innovation Strategy on Technological Convergence Performance: The Case of Korea’s ICT Industry
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Sustainability 2017, 9(9), 1582; doi:10.3390/su9091582

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

1
Department of Automotive Engineering, Hefei University of Technology, Hefei 230009, China
2
State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China
3
Information Technology Center, Tsinghua University, Beijing 100084, China
4
Collaborative Innovation Center for Electric Vehicles, Beijing 100084, China
5
Department of Electrical and Computer Engineering, the Ohio State University, Columbus, OH 43210, USA
*
Author to whom correspondence should be addressed.
Received: 17 August 2017 / Revised: 2 September 2017 / Accepted: 3 September 2017 / Published: 7 September 2017
View Full-Text   |   Download PDF [1007 KB, uploaded 7 September 2017]   |  

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. View Full-Text
Keywords: intelligent alternative-energy vehicles; situational assessments; uncertainty-risk awareness; infinite risk assessments intelligent alternative-energy vehicles; situational assessments; uncertainty-risk awareness; infinite risk assessments
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Xie, G.; Zhang, X.; Gao, H.; Qian, L.; Wang, J.; Ozguner, U. Situational Assessments Based on Uncertainty-Risk Awareness in Complex Traffic Scenarios. Sustainability 2017, 9, 1582.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sustainability EISSN 2071-1050 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top