# An Ethical Trajectory Planning Algorithm for Autonomous Vehicles

arXiv: https://arxiv.org/abs/2212.08577 · HTML: https://mlsanity.com/paper/2212.08577/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2022.

## Built on by (most cited first, 16 of 16)

- [A Platform-Agnostic Deep Reinforcement Learning Framework for Effective Sim2Real Transfer towards Autonomous Driving](https://mlsanity.com/paper/2304.08235/) (2023)
- [A Reinforcement Learning-Boosted Motion Planning Framework: Comprehensive Generalization Performance in Autonomous Driving](https://mlsanity.com/paper/2402.01465/) (2024)
- [When, Where, and What? A Novel Benchmark for Accident Anticipation and Localization with Large Language Models](https://mlsanity.com/paper/2407.16277/) (2024)
- [Prediction Horizon Requirements for Automated Driving: Optimizing Safety, Comfort, and Efficiency](https://mlsanity.com/paper/2402.03893/) (2024)
- [Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness](https://mlsanity.com/paper/2403.14641/) (2024)
- [RetroMotion: Retrocausal Motion Forecasting Models are Instructable](https://mlsanity.com/paper/2505.20414/) (2025)
- [BAP-SRL: Bayesian Adaptive Priority Safe Reinforcement Learning for Vehicle Motion Planning at Mixed Traffic Intersections](https://mlsanity.com/paper/2601.21679/) (2026)
- [An Online Spatial-Temporal Graph Trajectory Planner for Autonomous Vehicles](https://mlsanity.com/paper/2404.12256/) (2024)
- [Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving](https://mlsanity.com/paper/2508.14926/) (2025)
- [Towards Safe and Reliable Autonomous Driving: Dynamic Occupancy Set Prediction](https://mlsanity.com/paper/2402.19385/) (2024)
- [S4TP: Social-Suitable and Safety-Sensitive Trajectory Planning for Autonomous Vehicles](https://mlsanity.com/paper/2404.11946/) (2024)
- [Fundamentals of legislation for autonomous artificial intelligence systems](https://mlsanity.com/paper/2410.02769/) (2024)
- [How to Brake? Ethical Emergency Braking with Deep Reinforcement Learning](https://mlsanity.com/paper/2512.10698/) (2025)
- [Towards Lawful Autonomous Driving: Deriving Scenario-Aware Driving Requirements from Traffic Laws and Regulations](https://mlsanity.com/paper/2604.24562/) (2026)
- [An Ethical eValuation Agent (EeVA): Results of a Proof-of-Concept Test on a Prototype Agentic-like Workflow to Assist Ethical Deliberations](https://mlsanity.com/paper/2606.11218/) (2026)
- [Self-Aware Active Learning Enables Continual Improvement in Autonomous Driving](https://mlsanity.com/paper/2608.29772/) (2026)

## Abstract (arXiv)

With the rise of AI and automation, moral decisions are being put into the hands of algorithms that were formerly the preserve of humans. In autonomous driving, a variety of such decisions with ethical implications are made by algorithms for behavior and trajectory planning. Therefore, we present an ethical trajectory planning algorithm with a framework that aims at a fair distribution of risk among road users. Our implementation incorporates a combination of five essential ethical principles: minimization of the overall risk, priority for the worst-off, equal treatment of people, responsibility, and maximum acceptable risk. To the best of the authors' knowledge, this is the first ethical algorithm for trajectory planning of autonomous vehicles in line with the 20 recommendations from the EU Commission expert group and with general applicability to various traffic situations. We showcase the ethical behavior of our algorithm in selected scenarios and provide an empirical analysis of the ethical principles in 2000 scenarios. The code used in this research is available as open-source software.