# Driver Assistance Eco-driving and Transmission Control with Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2212.07594 · HTML: https://mlsanity.com/paper/2212.07594/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 15 Dec 2022.

## Placed in

- End-to-end autonomous driving / [Cooperative and V2X driving](https://mlsanity.com/driving/cooperative-and-v2x-driving/)

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Maximum a Posteriori Policy Optimisation](https://mlsanity.com/paper/1806.06920/) (2018)
- [Safe and Efficient Off-Policy Reinforcement Learning](https://mlsanity.com/paper/1606.02647/) (2016)
- [A Reinforcement Learning Based Approach for Automated Lane Change Maneuvers](https://mlsanity.com/paper/1804.07871/) (2018)
- [Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics](https://mlsanity.com/paper/2001.00449/) (2020)
- [Longitudinal Dynamic versus Kinematic Models for Car-Following Control Using Deep Reinforcement Learning](https://mlsanity.com/paper/1905.08314/) (2019)

## Built on by (most cited first, 3 of 3)

- [Optimal control barrier functions for RL based safe powertrain control](https://mlsanity.com/paper/2405.11391/) (2024)
- [Safe Reinforcement Learning for an Energy-Efficient Driver Assistance System](https://mlsanity.com/paper/2301.00904/) (2023)
- [Shared learning of powertrain control policies for vehicle fleets](https://mlsanity.com/paper/2404.17892/) (2024)

## Abstract (arXiv)

With the growing need to reduce energy consumption and greenhouse gas emissions, Eco-driving strategies provide a significant opportunity for additional fuel savings on top of other technological solutions being pursued in the transportation sector. In this paper, a model-free deep reinforcement learning (RL) control agent is proposed for active Eco-driving assistance that trades-off fuel consumption against other driver-accommodation objectives, and learns optimal traction torque and transmission shifting policies from experience. The training scheme for the proposed RL agent uses an off-policy actor-critic architecture that iteratively does policy evaluation with a multi-step return and policy improvement with the maximum posteriori policy optimization algorithm for hybrid action spaces. The proposed Eco-driving RL agent is implemented on a commercial vehicle in car following traffic. It shows superior performance in minimizing fuel consumption compared to a baseline controller that has full knowledge of fuel-efficiency tables.