mlsanityData to 14 Oct 2026

Autonomous Vehicles Path Planning under Temporal Logic Specifications

arXiv 2410.07845 ↗First posted 10 Oct 2024cs.RO cs.LOcited by 0

On the map: End-to-end autonomous driving → Imitation-learning end-to-end planners

Abstract (arXiv)

Path planning is an essential component of autonomous driving. A global planner is responsible for the high-level planning. It basically performs a shortest-path search on a known map, thereby defining waypoints used to control the local (low-level) planner. Local planning is a runtime verification method which is repeatedly run on the vehicle itself in real-time, so as to find the optimal short-horizon path which leads to the desired waypoint in a way which is both efficient and safe. The challenge is that the local planner has to take into account repeatedly incoming updates about the information available of the environment. In addition, it performs a complex task, as it has to take into account a large variety of requirements, originating from the necessity of collision avoidance with obstacles, respecting traffic rules, sticking to regulatory requirements, and lastly to reach the next waypoint efficiently. In this paper, we describe a logic-based specification mechanism which fulfills all these requirements.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Imitation-learning end-to-end planners, one of 9 directions in End-to-end autonomous driving. Builds on 5 of its 19 references that are in the map. First posted on arXiv 10 Oct 2024.

Builds on

5 of 19 references.

References · 5

  1. 2020On Robustness Metrics for Learning STL Taskscited by 27
  2. 2019Arithmetic-Geometric Mean Robustness for Control from Signal Temporal Logic Specificationscited by 19
  3. 2021Study of Signal Temporal Logic Robustness Metrics for Robotic Tasks Optimizationcited by 4
  4. 2018Generalizing Robot Imitation Learning with Invariant Hidden Semi-Markov Modelscited by 1
  5. 2022Optimizing Demonstrated Robot Manipulation Skills for Temporal Logic Constraintscited by 1

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  5. 2024Let Hybrid A* Path Planner Obey Traffic Rules: A Deep Reinforcement Learning-Based Planning Framework
  6. 2024A Rule-Based Behaviour Planner for Autonomous Driving
  7. 2025Human-like Semantic Navigation for Autonomous Driving using Knowledge Representation and Large Language Models
  8. 2021Safety and progress proofs for a reactive planner and controller for autonomous driving