The paper introduces a physics-informed neural cellular automaton (PI‑NCA) designed to learn traffic flow dynamics from data while respecting physical constraints such as vehicle conservation and road topology. By extending the model to stochastic dynamics, it can capture probabilistic transition rules without violating these constraints. Experiments on Nagel‑Schreckenberg and Kerner‑Klenov‑Wolf cellular automata show that PI‑NCA outperforms standard neural cellular automata in learning both deterministic and stochastic traffic models.
By Federica Bragone, Matthieu Barreau
The paper introduces the Markov Chain Car‑Following (MC‑CF) model, an empirical probabilistic approach that represents car‑following as a Markov process and samples accelerations from empirical distributions within discretized state bins. Evaluation on the Waymo Open Motion Dataset shows that MC‑CF variants outperform physics‑based baselines and compete with modern data‑driven methods in both one‑step and open‑loop trajectory prediction. Zero‑shot transfer to the Naturalistic Phoenix dataset and microscopic ring‑road simulations demonstrate cross‑domain generalization and scalability, with the model reducing collisions and reproducing naturalistic shockwave propagation.
By Sungyong Chung, Yanlin Zhang, Nachuan Li, Dana Monzer, Alireza Talebpour
Traffic microsimulators rely on hand-crafted behavior models that reproduce aggregate flow but miss the heterogeneous interactions between vehicles at signalized intersections. Learned trajectory predictors capture richer interactions but are short-horizon and tend to be unstable when run in closed loop.
arXiv:2610.27921v1 Announce Type: new
Abstract: A single observation can admit several plausible futures, making uncertainty an essential part of world modelling. However, quantum generative models r...
By Zhiyuan Liu, Sicheng Liu, Haiyu Wu
The paper introduces a plug‑and‑play method that injects traffic‑element signals—such as traffic lights and road signs—into end‑to‑end autonomous driving models with minimal architectural changes. By augmenting several public datasets with comprehensive traffic‑element annotations, the authors evaluate this integration across diverse driving paradigms, consistently improving performance on nuScenes, NAVSIM‑v1, NAVSIM‑v2, and Bench2Drive. The approach achieves a new state‑of‑the‑art result on the challenging NAVSIM‑v2 benchmark, demonstrating the broad utility of traffic‑element awareness.
arXiv:2509. 24725v4 Announce Type: replace-cross Abstract: Estimating queue lengths at signalized intersections is a long-standing challenge in traffic management.
By Ting Gao, Elvin Isufi, Winnie Daamen, Erik-Sander Smits, Serge Hoogendoorn