An Ultra-Light Heuristic Algorithm for Autonomous Optimal Eco-Driving

Connected autonomy brings with it the means of significantly increasing vehicle Energy Economy (EE) through optimal Eco-Driving control. Much research has been conducted in the area of autonomous Eco-Driving control via various methods. Generally, proposed algorithms fall into the broad categories of rules-based controls, optimal controls, and meta-heuristics. Proposed algorithms also vary in cost function type with the 2-norm of acceleration being common. In a previous study the authors classified and implemented commonly represented methods from the literature using real-world data. Results from the study showed a tradeoff between EE improvement and run-time and that the best overall performers were meta-heuristics. Results also showed that cost functions sensitive to the 1-norm of acceleration led to better performance than those which directly minimize the 2-norm. In this paper the authors present an ultra-light heuristic method for generating optimal Eco-Driving traces for Connected Autonomous Vehicles (CAVs) which indirectly minimizes the 1-norm of acceleration. This novel method produces EE improvements in line with previously implemented meta-heuristic methods while executing in a fraction of the time.

• Record URL:
  • https://doi.org/10.4271/2023-01-0679
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/01487191
• Supplemental Notes:
  • Abstract reprinted with permission of SAE International.
• Authors:
  • Rabinowitz, Aaron I
  • Motallebiaraghi, Farhang
  • Meyer, Rick
  • Asher, Zachary
  • Kolmanovsky, Ilya
  • Bradley, Thomas
• Conference:
  • WCX SAE World Congress Experience
  • Location: Detroit Michigan, United States
  • Date: 2023-4-18 to 2023-4-20
• Publication Date: 2023-4-11

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Serial:
  • SAE Technical Paper
  • Publisher: Society of Automotive Engineers (SAE)
  • ISSN: 0148-7191
  • EISSN: 2688-3627
  • Serial URL: http://papers.sae.org/

Subject/Index Terms

• TRT Terms: Acceleration (Mechanics); Autonomous vehicles; Connected vehicles; Ecodriving; Mathematical models
• Subject Areas: Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01882384
• Record Type: Publication
• Source Agency: SAE International
• Report/Paper Numbers: 2023-01-0679
• Files: TRIS, SAE
• Created Date: May 22 2023 1:28PM