# Energy-Efficient Autonomous Driving Using Cognitive Driver Behavioral Models and Reinforcement Learning

**Authors:** Huayi Li, Nan Li, Ilya Kolmanovsky, Anouck Girard  
**arXiv:** [2111.13966](https://arxiv.org/abs/2111.13966)  
**Date:** 2021-11-27  
**Summary page:** https://www.summarizepaper.com/en/paper/2111.13966v1/

## Key points

- Autonomous driving technologies have the potential to enhance mobility, road safety, and energy efficiency
- It is crucial to consider the interactions between autonomous vehicles (AVs) and human drivers for safety and energy consumption
- The proposed framework leverages cognitive hierarchy theory and reinforcement learning for developing energy-efficient AV policies on shared roads
- Cognitive hierarchy theory models decision-making processes of human drivers at different levels of rationality
- AVs can anticipate human driver actions and adjust their driving strategies accordingly by understanding their behavior
- Reinforcement learning allows AVs to continuously learn from their environment and improve decision-making abilities over time
- AVs can adapt to changing road conditions, optimize energy consumption, and ensure safe operation through reinforcement learning
- The framework combines cognitive hierarchy theory and reinforcement learning to develop comprehensive energy-efficient AV policies on shared roads.

## Summary

Autonomous driving technologies have the potential to not only enhance mobility and road safety but also bring about energy efficiency benefits. As autonomous vehicles (AVs) are expected to operate alongside human-driven vehicles in the near future, it is crucial to consider the interactions between AVs and surrounding human drivers in order to ensure safety and minimize energy consumption. In this chapter, the authors propose a framework for developing energy-efficient autonomous driving policies on shared roads by leveraging cognitive hierarchy theory and reinforcement learning. The framework takes into account the behavior of human drivers by employing cognitive hierarchy theory, which models decision-making processes based on different levels of rationality. By understanding how human drivers make choices on the road, AVs can better anticipate their actions and adjust their own driving strategies accordingly. This approach enables AVs to effectively navigate shared roads while maintaining safety and liveness. Reinforcement learning is another key component of the proposed framework. By using reinforcement learning algorithms, AVs can continuously learn from their interactions with the environment and improve their decision-making abilities over time. This allows them to adapt to changing road conditions and optimize their energy consumption while ensuring safe operation. Overall, this chapter presents a comprehensive framework that combines cognitive hierarchy theory and reinforcement learning to develop energy-efficient autonomous driving policies on shared roads. By considering human driver behavior and continuously improving through reinforcement learning, AVs can contribute to safer roads while also reducing energy consumption.