# Rationale-aware Autonomous Driving Policy utilizing Safety Force Field implemented on CARLA Simulator

arXiv: https://arxiv.org/abs/2211.10237 · HTML: https://mlsanity.com/paper/2211.10237/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Adversarial scenarios and safety testing, one of 9 directions in End-to-end autonomous driving.
- Builds on 17 of its 41 references that are in the map.
- First posted on arXiv 18 Nov 2022.

## Placed in

- End-to-end autonomous driving / [Adversarial scenarios and safety testing](https://mlsanity.com/driving/adversarial-scenarios-and-safety-testing/)

## Builds on (most cited first, 17 of 17)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [On a Formal Model of Safe and Scalable Self-driving Cars](https://mlsanity.com/paper/1708.06374/) (2017)
- [A Survey of Deep RL and IL for Autonomous Driving Policy Learning](https://mlsanity.com/paper/2101.01993/) (2021)
- [Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles](https://mlsanity.com/paper/1910.04586/) (2019)
- [Adaptive Stress Testing with Reward Augmentation for Autonomous Vehicle Validation](https://mlsanity.com/paper/1908.01046/) (2019)
- [Probabilistic Prediction of Vehicle Semantic Intention and Motion](https://mlsanity.com/paper/1804.03629/) (2018)
- [Simulation-based Adversarial Test Generation for Autonomous Vehicles with Machine Learning Components](https://mlsanity.com/paper/1804.06760/) (2018)
- [Autonomous Highway Driving using Deep Reinforcement Learning](https://mlsanity.com/paper/1904.00035/) (2019)
- [Deep Reinforcement Learning with Enhanced Safety for Autonomous Highway Driving](https://mlsanity.com/paper/1910.12905/) (2019)
- [Self-Driving like a Human driver instead of a Robocar: Personalized comfortable driving experience for autonomous vehicles](https://mlsanity.com/paper/2001.03908/) (2020)
- [Requirements-driven Test Generation for Autonomous Vehicles with Machine Learning Components](https://mlsanity.com/paper/1908.01094/) (2019)
- [Towards Learning Multi-agent Negotiations via Self-Play](https://mlsanity.com/paper/2001.10208/) (2020)
- [Predicting Vehicle Behaviors Over An Extended Horizon Using Behavior Interaction Network](https://mlsanity.com/paper/1903.00848/) (2019)
- [Fundamental Considerations around Scenario-Based Testing for Automated Driving](https://mlsanity.com/paper/2005.04045/) (2020)
- [Deep Reinforcement Learning for Human-Like Driving Policies in Collision Avoidance Tasks of Self-Driving Cars](https://mlsanity.com/paper/2006.04218/) (2020)
- [Towards Provably Not-at-Fault Control of Autonomous Robots in Arbitrary Dynamic Environments](https://mlsanity.com/paper/1902.02851/) (2019)
- [Online Verification Concept for Autonomous Vehicles -- Illustrative Study for a Trajectory Planning Module](https://mlsanity.com/paper/2005.07740/) (2020)

## Built on by (most cited first, 1 of 1)

- [A Review of Reward Functions for Reinforcement Learning in the context of Autonomous Driving](https://mlsanity.com/paper/2405.01440/) (2024)

## Abstract (arXiv)

Despite the rapid improvement of autonomous driving technology in recent years, automotive manufacturers must resolve liability issues to commercialize autonomous passenger car of SAE J3016 Level 3 or higher. To cope with the product liability law, manufacturers develop autonomous driving systems in compliance with international standards for safety such as ISO 26262 and ISO 21448. Concerning the safety of the intended functionality (SOTIF) requirement in ISO 26262, the driving policy recommends providing an explicit rational basis for maneuver decisions. In this case, mathematical models such as Safety Force Field (SFF) and Responsibility-Sensitive Safety (RSS) which have interpretability on decision, may be suitable. In this work, we implement SFF from scratch to substitute the undisclosed NVIDIA's source code and integrate it with CARLA open-source simulator. Using SFF and CARLA, we present a predictor for claimed sets of vehicles, and based on the predictor, propose an integrated driving policy that consistently operates regardless of safety conditions it encounters while passing through dynamic traffic. The policy does not have a separate plan for each condition, but using safety potential, it aims human-like driving blended in with traffic flow.