# Safe Reinforcement Learning for an Energy-Efficient Driver Assistance System

arXiv: https://arxiv.org/abs/2301.00904 · HTML: https://mlsanity.com/paper/2301.00904/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 3 Jan 2023.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Control Barrier Functions: Theory and Applications](https://mlsanity.com/paper/1903.11199/) (2019)
- [Maximum a Posteriori Policy Optimisation](https://mlsanity.com/paper/1806.06920/) (2018)
- [End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks](https://mlsanity.com/paper/1903.08792/) (2019)
- [Safe and Efficient Off-Policy Reinforcement Learning](https://mlsanity.com/paper/1606.02647/) (2016)
- [Safe Exploration in Continuous Action Spaces](https://mlsanity.com/paper/1801.08757/) (2018)
- [Recovery RL: Safe Reinforcement Learning with Learned Recovery Zones](https://mlsanity.com/paper/2010.15920/) (2020)
- [Learning to be Safe: Deep RL with a Safety Critic](https://mlsanity.com/paper/2010.14603/) (2020)
- [Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics](https://mlsanity.com/paper/2001.00449/) (2020)
- [Comparison between safety methods control barrier function vs. reachability analysis](https://mlsanity.com/paper/2106.13176/) (2021)
- [Driver Assistance Eco-driving and Transmission Control with Deep Reinforcement Learning](https://mlsanity.com/paper/2212.07594/) (2022)

## Built on by (most cited first, 2 of 2)

- [CBF-RL: Safety Filtering Reinforcement Learning in Training with Control Barrier Functions](https://mlsanity.com/paper/2510.14959/) (2025)
- [Shared learning of powertrain control policies for vehicle fleets](https://mlsanity.com/paper/2404.17892/) (2024)

## Abstract (arXiv)

Reinforcement learning (RL)-based driver assistance systems seek to improve fuel consumption via continual improvement of powertrain control actions considering experiential data from the field. However, the need to explore diverse experiences in order to learn optimal policies often limits the application of RL techniques in safety-critical systems like vehicle control. In this paper, an exponential control barrier function (ECBF) is derived and utilized to filter unsafe actions proposed by an RL-based driver assistance system. The RL agent freely explores and optimizes the performance objectives while unsafe actions are projected to the closest actions in the safe domain. The reward is structured so that driver's acceleration requests are met in a manner that boosts fuel economy and doesn't compromise comfort. The optimal gear and traction torque control actions that maximize the cumulative reward are computed via the Maximum a Posteriori Policy Optimization (MPO) algorithm configured for a hybrid action space. The proposed safe-RL scheme is trained and evaluated in car following scenarios where it is shown that it effectively avoids collision both during training and evaluation while delivering on the expected fuel economy improvements for the driver assistance system.