PV-WM is a history‑only world model that jointly predicts pedestrian root motion, 15‑joint articulation, and vehicle kinematic states in a synchronized heterogeneous state. It uses recurrent updates to generate pedestrian and vehicle motion chunks, reconstructing vehicle boxes from predicted center, heading, and observed extent, and recomputes pedestrian‑vehicle geometry after each transition. Compared to a one‑shot predictor, PV‑WM reduces Root ADE by 12.7% and MPJPE by 14.8%, and across 824 Waymo contexts it lowers Root ADE by 5.2%, MPJPE by 7.6%, P‑V distance error by 11.9%, and oriented‑box closest‑approach error by 5.8%, while using 57.1% fewer parameters, 96.5% fewer FLOPs, and 25.5% lower p95 latency.
By Haozhuang Chi, Jingsong Liang, Ziying Song, Lei Yang, Shihao Li, Haoruo Zhang, Chen Lv
arXiv:2607. 08316v1 Announce Type: new Abstract: Vehicle intention prediction is a pivotal aspect in the agility and safety of autonomous vehicles in all driving scenarios; if genuine enhancement of autonomous vehicles are required, we need to make them adopt human interpretation of driver's intention especially in cases that require a lot of human interaction as well as complex driving behaviors like the ones at intersections, roundabouts and emergency cases such as sudden stops where vehicle intention prediction helps in taking the correct evasive action within a real time period where every second of action makes an impact and can prevent a catastrophe from taking place.
By Logine M. Zaki, Catherine M. Elias
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:2607. 20505v1 Announce Type: cross Abstract: Surrogate safety measures (SSMs) enable proactive traffic safety assessment, but many existing methods evaluate pairwise interactions independently or flatten multi-agent scenes into fixed feature vectors, limiting their ability to represent heterogeneous interaction structure and evolving scene-level risk.
By Md Monzurul Islam, Subasish Das
arXiv:2606. 12500v1 Announce Type: cross Abstract: Traffic microsimulation combined with surrogate safety measures has increasingly been used as a proactive alternative to historical crash data for predicting crash frequency for current or planned road infrastructure designs.
By Xian Liu, Carlo G. Prato, Gustav Markkula
arXiv:2606. 00857v1 Announce Type: cross Abstract: Accurate and reliable vehicle trajectory prediction is essential for safe autonomous driving.
By Xinyi Ning, Zilin Bian, Dachuan Zuo, Semiha Ergan, Kaan Ozbay
The paper presents an end‑to‑end system that converts driving footage into dynamic vision sensor (DVS) event streams, augments training with simulated DVS data, and trains a convolutional spiking neural network (Conv‑SNN) to classify pedestrian crossing intent as crossing or non‑crossing. The Conv‑SNN, trained with a class‑balanced loss and surrogate‑gradient learning, achieves high accuracy and F1 scores on JAAD and CARLA DVS datasets, outperforming or matching prior frame‑based methods while operating on sparse temporal representations. The study details architectural choices, neuron dynamics, and training protocols, and provides a convergence analysis and domain‑transfer evaluation.
By Henok Teklu, Mustafa Sakhai, Maciej Wielgosz, Matej Mertik
arXiv:2607. 16156v1 Announce Type: new Abstract: Urban intersections are among the most hazardous locations in road networks, posing significant risks to vehicles and vulnerable road users (VRUs) such as pedestrians and cyclists.
By Tam Bang, Hussam Abubakr, Emiliano de la Garza Villarreal, Truc Phuong Nguyen, Austin Harris, Toru Hirano, Mina Sartipi, Yunfei Xu, Hoang H. Nguyen
arXiv:2606. 18824v1 Announce Type: cross Abstract: Pedestrian trajectory prediction from an ego-centric camera is challenging since it depends on complex interactions with vehicles and scene context, as well as the intention of the pedestrian.
By Yuxuan Xie, Nicolas Pugeault, Chongfeng Wei, Hubert P. H. Shum, Edmond S. L. Ho
PRISM (Proactive Risk Intelligence and Safety Management) is an agentic multi-model architecture designed to shift autonomous transportation safety from reactive crash avoidance to proactive, continuous risk management. It uses inverse crash‑probability modeling to transform binary crash classifiers into dynamic safety scores, and runs three specialized models—trajectory kinematics, environmental risk, and VRU interaction—coordinated by a reinforcement‑learning reasoning layer. Across 1,296 naturalistic driving scenarios, PRISM achieved a mean safety score of 68/100, classified 77.6% of situations as advisory, and flagged 3.8% as near‑misses, with 11% requiring intervention or emergency response, highlighting trajectory risk and VRU proximity as key safety factors.
By Joyjit Roy, Samaresh Kumar Singh, Sushanta Das
arXiv:2609.16415v1 Announce Type: cross
Abstract: Pedestrian-count forecasting supports pedestrian-oriented Intelligent Transportation Systems (ITS), including crowd monitoring, pedestrian-traffic st...
By Theivaprakasham Hari, Ziteng Li, Yanan Xin, Winnie Daamen, Serge Hoogendoorn
arXiv:2603. 14841v3 Announce Type: replace-cross Abstract: Road crashes remain a leading cause of preventable fatalities.
By Joyjit Roy, Samaresh Kumar Singh, Sushanta Das