Energy-Efficient Autonomous Driving Using Cognitive Driver Behavioral Models and Reinforcement Learning
On the map: End-to-end autonomous driving → Cooperative and V2X driving
Abstract (arXiv)
Autonomous driving technologies are expected to not only improve mobility and road safety but also bring energy efficiency benefits. In the foreseeable future, autonomous vehicles (AVs) will operate on roads shared with human-driven vehicles. To maintain safety and liveness while simultaneously minimizing energy consumption, the AV planning and decision-making process should account for interactions between the autonomous ego vehicle and surrounding human-driven vehicles. In this chapter, we describe a framework for developing energy-efficient autonomous driving policies on shared roads by exploiting human-driver behavior modeling based on cognitive hierarchy theory and reinforcement learning.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving. Builds on 13 of its 50 references that are in the map. First posted on arXiv 27 Nov 2021.
Builds on
13 of 50 references.
References · 13
- 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 721
- 2019A Survey of Deep Learning Techniques for Autonomous Drivingcited by 403
- 2019A Survey of Deep Learning Applications to Autonomous Vehicle Controlcited by 106
- 2016Game-Theoretic Modeling of Driver and Vehicle Interactions for Verification and Validation of Autonomous Vehicle Control Systemscited by 24
- 2018Control of Connected and Automated Vehicles: State of the Art and Future Challengescited by 11
- 2019Game-Theoretic Modeling of Multi-Vehicle Interactions at Uncontrolled Intersectionscited by 11
- 2019Game-theoretic Modeling of Traffic in Unsignalized Intersection Network for Autonomous Vehicle Control Verification and Validationcited by 8
- 2020Decentralized Optimal Coordination of Connected and Automated Vehicles for Multiple Traffic Scenarioscited by 3
- 2019Combined Energy and Comfort Optimization of Air Conditioning System in Connected and Automated Vehiclescited by 1
- 2020Decentralized Cooperative Merging of Platoons of Connected and Automated Vehicles at Highway On-Rampscited by 1
- 2020Combined Eco-Routing and Power-Train Control of Plug-In Hybrid Electric Vehicles in Transportation Networkscited by 1
- 2021Coordinated Receding-Horizon Control of Battery Electric Vehicle Speed and Gearshift Using Relaxed Mixed Integer Nonlinear Programmingcited by 1
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- 2024A Scoping Review of Energy-Efficient Driving Behaviors and Applied State-of-the-Art AI Methods
- 2026Distill to Think, Foresee to Act: Cognitive-Physical Reinforcement Learning for Autonomous Driving
- 2022Cognitive Level-k Meta-Learning for Safe and Pedestrian-Aware Autonomous Driving
- 2023Developing Driving Strategies Efficiently: A Skill-Based Hierarchical Reinforcement Learning Approach
- 2022Learning Eco-Driving Strategies at Signalized Intersections
- 2023Towards Socially Responsive Autonomous Vehicles: A Reinforcement Learning Framework with Driving Priors and Coordination Awareness
- 2025Toward Safety-First Human-Like Decision Making for Autonomous Vehicles in Time-Varying Traffic Flow
- 2025Impact of Collective Behaviors of Autonomous Vehicles on Urban Traffic Dynamics: A Multi-Agent Reinforcement Learning Approach