mlsanityData to 7 Oct 2026

Kernel-Based Metrics Learning for Uncertain Opponent Vehicle Trajectory Prediction in Autonomous Racing

arXiv 2609.17147 ↗First posted 15 Sep 2026cs.RO cs.AI cs.LGcited by 0

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

Abstract (arXiv)

Autonomous racing confronts significant challenges in safely overtaking Opponent Vehicles (OVs) that exhibit uncertain trajectories, stemming from unknown driving policies. To address these challenges, this study proposes heterogeneous kernel metrics for Deep Kernel Learning (DKL), designed to robustly capture the diverse driving policies of OVs, and carry out precise trajectory predictions along with the associated uncertainties. A key virtue of the proposed kernel metrics lies in their ability to align similar driving policies and disjoin dissimilar ones in an unsupervised manner, given the observed interactions between the Ego Vehicle (EV) and OVs. The efficacy of the proposed method is substantiated through experimental studies on a 1/10th scale racecar platform, demonstrating improved prediction accuracy and thereby safely overtaking against OVs. Furthermore, our method is computationally efficient for onboard computing units, affirming its viability in fast-paced racing environments. The video and source code can be found at https://github.com/HMCL-UNIST/OpponentPredictionWithKMDKL.git.

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 12 of its 23 references that are in the map. First posted on arXiv 15 Sep 2026.

Builds on

12 of 23 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing14 Feb

Other references · 11

  1. 2018GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Accelerationcited by 603
  2. 2015Deep Kernel Learningcited by 440
  3. 2021MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Predictioncited by 246
  4. 2021The Promises and Pitfalls of Deep Kernel Learningcited by 81
  5. 2024UniTraj: A Unified Framework for Scalable Vehicle Trajectory Predictioncited by 51
  6. 2017Optimization-Based Autonomous Racing of 1:43 Scale RC Carscited by 47
  7. 2021Interactive multi-modal motion planning with Branch Model Predictive Controlcited by 21
  8. 2022Uncertainty estimation for Cross-dataset performance in Trajectory predictioncited by 13
  9. 2022A Gaussian Process Model for Opponent Prediction in Autonomous Racingcited by 5
  10. 2023Out of Distribution Detection via Domain-Informed Gaussian Process State Space Modelscited by 3
  11. 2022Frenet-Cartesian Model Representations for Automotive Obstacle Avoidance within Nonlinear MPCcited by 2

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  1. 2021Track based Offline Policy Learning for Overtaking Maneuvers with Autonomous Racecars
  2. 2026Autonomous Vehicle Collision Avoidance With Racing Parameterized Deep Reinforcement Learning
  3. 2023Machine Learning for Autonomous Vehicle's Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directions
  4. 2024Deep Reinforcement Learning for Local Path Following of an Autonomous Formula SAE Vehicle
  5. 2024A Simulation Benchmark for Autonomous Racing with Large-Scale Human Data
  6. 2024Towards an Autonomous Test Driver: High-Performance Driver Modeling via Reinforcement Learning
  7. 2023Partial End-to-end Reinforcement Learning for Robustness Against Modelling Error in Autonomous Racing
  8. 2024F1tenth Autonomous Racing With Offline Reinforcement Learning Methods