The paper presents a single pretrained diffusion traffic model that serves both as an ego motion planner and as a controllable generator of safety‑critical scenarios for autonomous driving. It introduces a Single‑Stream Dual‑Stream diffusion‑transformer decoder (SSDS) that fuses scene context via joint attention, improving closed‑loop performance on the nuPlan benchmark, and a training‑free guidance scheme called Decoupled Annealing Posterior Sampling with Energy (DAPSE) that injects arbitrary energy functions at inference time. Using the same model, the authors generate realistic long‑tail driving interactions—such as aggressive cut‑ins and lead‑vehicle braking—through inference‑time guidance, exposing failure modes in black‑box planners that standard benchmarks miss.
By Arka Pal, Rajesh Kumar, Hannes Eriksson, R\'emi Lacombe, Arvid Laveno Ling, Ankit Gupta, Maciej Wozniak
arXiv:2603. 10330v2 Announce Type: replace-cross Abstract: Autonomous driving in complex traffic requires planners that generalize beyond hand-crafted rules, motivating data-driven approaches that learn behavior from expert demonstrations.
By Eugene Ku, Yiwei Lyu
arXiv:2601.01762v4 Announce Type: replace-cross
Abstract: Practical autonomous driving requires models that generalize by reasoning through spatial-temporal possibilities to exclude unsafe outcomes....
By Yanhao Wu, Haoyang Zhang, Fei He, Rui Wu, Yanhu Shan, Congpei Qiu, Liang Gao, Wei Ke, Tong Zhang
arXiv:2609.39995v2 Announce Type: new
Abstract: Diffusion planners exhibit strong capabilities in generating multimodal trajectories. However, existing methods primarily rely on expert demonstrations...
By Jiaxi Ye, Chunji Lv, Guoren Wang, Changsheng Li
The paper presents a training-free diffusion-based motion planner that replaces learned global trajectory scores with analytical local scores derived from obstacle, smoothness, velocity, and inter-agent feasibility terms. By reconstructing trajectory scores through local interactions between neighboring waypoints and nearby constraints, the method decomposes the denoising process while preserving the optimization structure of classical trajectory methods. Experiments demonstrate that this approach generates smooth, feasible trajectories for large multi-agent tasks in complex environments quickly, outperforming learning-based and optimization baselines without requiring training data.
By Michael Y. Fatemi, Jinhao Liang, Ferdinando Fioretto
arXiv:2606. 27766v1 Announce Type: cross Abstract: Offline reinforcement learning enables policy learning from fixed datasets without additional environment interaction, making it appealing for safety-critical applications where online exploration is costly or unsafe.
By Shiqiang Gong
BridgeGuard is a safety‑constrained diffusion planning method for autonomous driving that progressively strengthens a constraint term during denoising, guiding intermediate trajectories toward a scene‑dependent safety domain. It operates in a low‑dimensional curve space and uses a learned DistanceFieldNet to predict a time‑dependent distance field from bird’s‑eye‑view features, providing safety injection while keeping the perception backbone and planner frozen. Experiments on Bench2Drive show that BridgeGuard improves driving scores and success rates across two diffusion‑based models, demonstrating cross‑model generalization.
By Zhenjun Qiu, Jianing Huang, Dongang Liu, Baiyu Du, Yixun Niu, Hao Yang, Xinyu Huang, Chuan Hu, Shu Liu
arXiv:2606. 11019v1 Announce Type: cross Abstract: Learning-based motion planners, despite recent progress, often suffer from temporal inconsistency.
By Zehan Zhang, Neng Zhang, Yaoyi Li, Jia Cai, Zhiling Wang
Expand-and-Prune is a test‑time framework for candidate‑based end‑to‑end autonomous driving planners that dynamically expands and prunes the set of trajectory hypotheses. It adds new candidates guided by semantic driving intent and temporally propagated predictions, then uses uncertainty in intermediate trajectory scores to prune the expanded set. Across four planners and four benchmarks, the method consistently improves planning performance, achieving state‑of‑the‑art results on LangAuto and significant gains on Bench2Drive, Fail2Drive, and NAVSIMv2, especially in out‑of‑distribution scenarios.
By Fabian Schmidt, Karol Fedurko, Markus Enzweiler, Abhinav Valada
The paper introduces Planning Under Noise (PUN), a neural operator that learns noise‑aware value fields for planning in unseen geometries. By imposing reflecting Neumann conditions on obstacles and a Dirichlet condition at the goal, PUN allows sampled paths to explore free space until reaching the target. It extends the Monte Carlo solver Walk on Stars to higher dimensions, learns geometry‑to‑field mappings from noisy labels, and achieves high success rates and low runtimes on 4‑D replanning benchmarks across multiple noise levels.
By Michael Chen, Hong Chul Nam, Xi Deng, Anima Anandkumar
arXiv:2607. 19919v1 Announce Type: cross Abstract: We propose Diffusion ReRoll, a diffusion-based framework for robotic sequential prediction that enables revisable denoising over horizons.
By Seonsoo Kim, Seongil Hong, Jun-Gill Kang
The paper introduces Endpoint-Constrained Optimization (ECO), a lightweight postprocessing layer that corrects intermediate waypoints of end-to-end driving policies while preserving the predicted endpoint. ECO does not require maps, privileged simulator state, or additional training, and can be applied to a wide range of waypoint-emitting policies. Experiments on two closed-loop simulators show that ECO significantly improves closed-loop performance, achieving top results in the HUGSIM Closed-Loop Driving Challenge and boosting scene scores on AlpaSim.
By Brayden Zhang, Mahsa Golchoubian, Igor Gilitschenski, Boris Ivanovic, Kashyap Chitta