An Ultra-Light Heuristic Algorithm for Autonomous Optimal Eco-Driving 2023-01-0679
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.
Citation: Rabinowitz, A., Motallebiaraghi, F., Meyer, R., Asher, Z. et al., "An Ultra-Light Heuristic Algorithm for Autonomous Optimal Eco-Driving," SAE Technical Paper 2023-01-0679, 2023, https://doi.org/10.4271/2023-01-0679. Download Citation
Author(s):
Aaron I. Rabinowitz, Farhang Motallebiaraghi, Rick Meyer, Zachary Asher, Ilya Kolmanovsky, Thomas Bradley
Affiliated:
Colorado State University, Western Michigan University, University of Michigan
Pages: 12
Event:
WCX SAE World Congress Experience
ISSN:
0148-7191
e-ISSN:
2688-3627
Related Topics:
Autonomous vehicles
Vehicle acceleration
Mathematical models
Research and development
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