Guiding End-to-End Driving Models with Endpoint-Constrained Trajectory Optimization

arXiv:2609.31383 · cs.RO, cs.AI, cs.CV, cs.LG · Submitted 2026-09-25 · Read on arXiv

cs.RO, cs.AI, cs.CV, cs.LG

Submitted: 2026-09-25

Updated: 2026-09-25

Code: https://github.com/NVlabs/alpasim

Project page: https://realadsim.github.io/2025

Terminology

• Benchmark — 4× in this paper · explained in 2 episodes
• Endpoint-Constrained Optimization — 4× in this paper
• Ablation — 2× in this paper · explained in 1 episode
• Fidelity — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Flow Matching — 2× in this paper · explained in 8 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• 3D Gaussian Splatting — 1× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Action Chunking — 1× in this paper · explained in 1 episode
• Actor — 1× in this paper · explained in 3 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Covariate Shift — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Gaussian Splatting — 1× in this paper · explained in 4 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• IEEE Intelligent Vehicles Symposium — 1× in this paper
• Latent Space — 1× in this paper · explained in 7 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Regret — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Steering — 1× in this paper · explained in 2 episodes

Sources

• VaViM and VaVAM: Autonomous Driving through Video Generative Modeling
• HUGSIM: A Real-Time, Photo-Realistic and Closed-Loop Simulator for Autonomous Driving
• Steering Your Diffusion Policy with Latent Space Reinforcement Learning
• Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation
• CGD: Constraint-Guided Diffusion Policies for UAV Trajectory Planning
• Test-Time Trajectory Optimization for Autonomous Driving
• OPTED: On-Policy Fine-Tuning for End-to-End Driving using a Render-Free Teacher
• NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles

Related papers

• FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
• MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
• RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
• HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
• APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
• Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving