Building an augmented map for road risk assessment

This thesis aims at demonstrating that augmented perception has better performance than noncooperative approaches, and that it can be used to successfully identify road risk. We provide at first a discussion on these limitations and a performance model built to incorporate them, created from empirical data collected on test tracks. Our results are more pessimistic than existing literature, suggesting intervehicular communication technology (IVC) limitations have been underestimated. Then, we develop a new Cooperative Systems (CS)-applications simulation architecture. This architecture is used to obtain new results on the safety benefits of a cooperative safety application (EEBL), and then to support further study on augmented perception. In the next step, we implement an augmented perception architecture tasked with creating an augmented map. Our approach is aimed at providing a generalist architecture that can use many different types of sensors to create the map, and which is not limited to any specific application. Then, augmented and single-vehicle perceptions are compared in a reference driving scenario for risk assessment, taking into account the IVC limitations obtained earlier; we show their impact on the augmented map's performance. Our results show that augmented perception performs better than non-cooperative approaches, allowing to almost tripling the advance warning time before a crash. Eventually, we propose a new approach using augmented perception to identify road risk through a surrogate: near-miss events. A CS-based approach is designed and validated to detect near-miss events, and then compared to a non-cooperative approach based on vehicles equipped with local sensors only. The cooperative approach shows a significant improvement in the number of events that can be detected, especially at the higher rates of system's deployment.

• Record URL:
  http://eprints.qut.edu.au/63669/
• Supplemental Notes:
  • PhD thesis
• Authors:
  • Demmel, S
• Publication Date: 2013-10

Language

• English

Media Info

• Pagination: 1 file

Subject/Index Terms

• TRT Terms: Advanced driver information systems; Advanced driver information systems; Communication; Perception; Risk; Risk assessment; Sensors; System architecture; Vehicle to vehicle communications; Vehicles; Warning signals
• Uncontrolled Terms: Safe systems (road users)
• ATRI Terms: Advanced driver information systems (ADIS); Perception; Risk assessment; Sensor; System architecture; Warning
• ITRD Terms: 8763: Driver information; 8735: Intelligent transport system; 9113: Warning
• Subject Areas: Vehicles and Equipment; I83: Accidents and the Human Factor;

Filing Info

• Accession Number: 01534341
• Record Type: Publication
• Source Agency: ARRB
• Files: ITRD, ATRI
• Created Date: Aug 11 2014 2:47PM