mlsanityData to 7 Oct 2026

Adaptive Autopilot: Constrained DRL for Diverse Driving Behaviors

arXiv 2407.02546 ↗First posted 2 Jul 2024cs.RO cs.LGcited by 0

On the map: End-to-end autonomous driving → Reinforcement learning for driving

Abstract (arXiv)

In pursuit of autonomous vehicles, achieving human-like driving behavior is vital. This study introduces adaptive autopilot (AA), a unique framework utilizing constrained-deep reinforcement learning (C-DRL). AA aims to safely emulate human driving to reduce the necessity for driver intervention. Focusing on the car-following scenario, the process involves (i) extracting data from the highD natural driving study and categorizing it into three driving styles using a rule-based classifier; (ii) employing deep neural network (DNN) regressors to predict human-like acceleration across styles; and (iii) using C-DRL, specifically the soft actor-critic Lagrangian technique, to learn human-like safe driving policies. Results indicate effectiveness in each step, with the rule-based classifier distinguishing driving styles, the regressor model accurately predicting acceleration, outperforming traditional car-following models, and C-DRL agents learning optimal policies for humanlike driving across styles.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving. Builds on 9 of its 31 references that are in the map. First posted on arXiv 2 Jul 2024.

Builds on

9 of 31 references.

References · 9

  1. 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 698
  2. 2019A Survey of Autonomous Driving: Common Practices and Emerging Technologiescited by 373
  3. 2019On The Power of Curriculum Learning in Training Deep Networkscited by 285
  4. 2018The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systemscited by 276
  5. 2020Learning to Walk in the Real World with Minimal Human Effortcited by 103
  6. 2019Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous Drivingcited by 56
  7. 2019Human-Like Autonomous Car-Following Model with Deep Reinforcement Learningcited by 47
  8. 2021Direct Behavior Specification via Constrained Reinforcement Learningcited by 30
  9. 2023Car-Following Models: A Multidisciplinary Reviewcited by 7

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2023Review of Deep Reinforcement Learning for Autonomous Driving
  2. 2021Human-in-the-Loop Deep Reinforcement Learning with Application to Autonomous Driving
  3. 2021A Survey of Deep RL and IL for Autonomous Driving Policy Learning
  4. 2025Evaluating Scenario-based Decision-making for Interactive Autonomous Driving Using Rational Criteria: A Survey
  5. 2021Modified DDPG car-following model with a real-world human driving experience with CARLA simulator
  6. 2021Efficient Deep Reinforcement Learning with Imitative Expert Priors for Autonomous Driving
  7. 2024HAIM-DRL: Enhanced Human-in-the-loop Reinforcement Learning for Safe and Efficient Autonomous Driving
  8. 2022Bilateral Deep Reinforcement Learning Approach for Better-than-human Car Following Model