mlsanityData to 7 Oct 2026

An Ethical Trajectory Planning Algorithm for Autonomous Vehicles

arXiv 2212.08577 ↗First posted 16 Dec 2022cs.RO cs.CYcited by 15

Not on the map. Why?

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.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Dec 2022.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 8; 2025: 3; 2026: 3.

15 papers.

Sort:

By area:

  1. 2023A Platform-Agnostic Deep Reinforcement Learning Framework for Effective Sim2Real Transfer towards Autonomous Drivingcited by 7
  2. 2024A Reinforcement Learning-Boosted Motion Planning Framework: Comprehensive Generalization Performance in Autonomous Drivingcited by 4
  3. 2024When, Where, and What? A Novel Benchmark for Accident Anticipation and Localization with Large Language Modelscited by 4
  4. 2024Prediction Horizon Requirements for Automated Driving: Optimizing Safety, Comfort, and Efficiencycited by 3
  5. 2024Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairnesscited by 3
  6. 2025RetroMotion: Retrocausal Motion Forecasting Models are Instructablecited by 2
  7. 2024An Online Spatial-Temporal Graph Trajectory Planner for Autonomous Vehiclescited by 1
  8. 2025Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Drivingcited by 1
  9. 2024Towards Safe and Reliable Autonomous Driving: Dynamic Occupancy Set Predictioncited by 0
  10. 2024S4TP: Social-Suitable and Safety-Sensitive Trajectory Planning for Autonomous Vehiclescited by 0
  11. 2024Fundamentals of legislation for autonomous artificial intelligence systemscited by 0
  12. 2025How to Brake? Ethical Emergency Braking with Deep Reinforcement Learningcited by 0
3 more
  1. 2026Towards Lawful Autonomous Driving: Deriving Scenario-Aware Driving Requirements from Traffic Laws and Regulationscited by 0
  2. 2026An Ethical eValuation Agent (EeVA): Results of a Proof-of-Concept Test on a Prototype Agentic-like Workflow to Assist Ethical Deliberationscited by 0
  3. 2026Self-Aware Active Learning Enables Continual Improvement in Autonomous Drivingcited by 0