Quantitative Analysis of the Impact of Baidu Apollo Parameterization on Trajectory Planning in a Critical Scenario

The increasing demand for reliable and safe high-driving automation algorithms in Autonomous Driving (AD) vehicles has driven significant advancements in the industry. This paper investigates the performance of a specific AD vehicle architecture in a critical lane change maneuver, extracted from the HighD Dataset, which consists of naturalistic vehicle trajectories recorded on German highways. The analysis focuses on different parameter configurations to understand their influence on the results. The selected AD algorithm is based on the Apollo Open Autonomous Driving Platform (AOADP). Simulation results demonstrate the significant impact of path planning algorithm parameters on lane change execution. The comparison between human driving behavior and AD systems plays a crucial role in determining sensing technology specifications. This approach provides a quantitative analysis of the impact on autonomous driving measurements, contributing to the safe specification of AD functions. These findings lay the foundation for future evaluations and improvements in the selected AD architecture.

• Record URL:
  • https://doi.org/10.1016/j.trpro.2023.11.897
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S2352146523011961
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/23521465
• Supplemental Notes:
  • © 2023 The Author(s). Published by Elsevier B.V. Abstract reprinted with permission of Elsevier.
• Authors:
  • Li, Hexuan
  • De Cristofaro, Francesco
  • Orucevic, Faris
  • Gu, Zhengguo
  • Eichberger, Arno
• Conference:
  • The Science and Development of Transport – Znanost i razvitak prometa - ZIRP 2023
  • Location: Zagreb , Croatia
  • Date: 2023-12-7 to 2023-12-8
• Publication Date: 2023

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 102-109
• Serial:
  • Transportation Research Procedia
  • Volume: 73
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2352-1465
  • Serial URL: http://www.sciencedirect.com/science/journal/23521465/
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Driving behavior; Lane changing; Simulation; Trajectory control; Vehicle safety
• Geographic Terms: Germany
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01911903
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 15 2024 4:34PM