The paper proposes a graph-based method for jointly controlling dynamic lane reversal and variable speed limits in traffic networks, ensuring the network remains strongly connected during lane changes. It introduces a transformer model with Global Endpoint Relation-Aware Spatio-Temporal Self-Attention (GERASTSA) that is pretrained on traffic forecasting and fine-tuned with proximal policy optimization. Experiments on a 6×6 grid show the learned policy can make two-thirds of roads one-way during peak hours, reducing daily delay by up to 90% compared to a fixed configuration.
By Hamed Araab, Abbas Ahmadi, Mohammad Hossein Basiri
arXiv:2412.02520v4 Announce Type: replace-cross
Abstract: Connected automated vehicles (CAVs) equipped with adaptive cruise control (ACC) create new opportunities for highway congestion mitigation. T...
By Yaron Veksler, Sharon Hornstein, Han Wang, Maria Laura Delle Monache, Daniel Urieli
arXiv:2607. 09740v1 Announce Type: new Abstract: Safe motion planning in advanced driver-assistance systems and autonomous vehicles requires an accurate understanding of how the surrounding traffic scene is likely to evolve.
By Joshua Kofi Asamoah, Blessing Agyei Kyem, Eugene Denteh, Armstrong Aboah
GRID‑Planner introduces a graph‑structured relational reward shaping framework for diffusion‑based autonomous driving planning. By integrating graph‑aware importance weighting, a time‑indexed relational reward surrogate, and an advantage‑weighted diffusion objective with on‑policy self‑distillation, it forms a rollout‑return‑advantage‑update loop that eliminates explicit likelihoods. Experiments on nuPlan and interPlan demonstrate significant reductions in collision rate, lane deviation, and jerk, along with a 3.4% improvement in overall planning score.
By Zhao Yang, Zezhong Qian, Yinan Shi, Longjun Liu, Yawei Jueluo
arXiv:2607. 03703v1 Announce Type: new Abstract: Reinforcement Learning (RL) has emerged as a powerful paradigm for adaptive traffic signal control.
By Dickens Kwesiga, Nishu Choudhary, Angshuman Guin, Michael Hunter
The paper introduces a gated graph attention network (GAT) to better explain spatial information flow in short‑term traffic forecasting models. By adding a gate that learns how much of a sensor’s updated state comes from its neighbors versus itself, the authors can progressively withdraw neighbor information and observe its impact on accuracy. Applied to the ST‑MetaNet architecture on data from 498 loop detectors in England, the gated GAT shows that neighbor influence is higher for busy sensors, improves accuracy slightly with mild regularisation, and reveals that the two GAT layers are largely redundant.
By Yue Li, Shujuan Chen, Ying Jin
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.
The paper presents a lane‑change prediction model using knowledge‑graph embeddings and Bayesian inference to anticipate surrounding vehicles’ intentions 3–4 seconds before they occur. Three driving strategies—reactive, prediction‑based longitudinal, and prediction‑based lateral—are compared in CARLA simulations of risky merging scenarios. Prediction‑based approaches avoid collisions, reduce peak deceleration by up to 91 %, and are evaluated for ride comfort using ISO 2631‑1 vibration estimates.
By Mohamed Manzour, Aditya Kumar, Rub\'en Izquierdo, Miguel \'Angel Sotelo
The paper introduces Anticipatory Generative Modeling, a new graph-constrained approach for generating road‑network trajectories that focuses on behavior modeling rather than simple location simulation. It presents Foresight, a probabilistic model that iteratively anticipates, selects, updates, and re‑anticipates future transitions to produce realistic routes that stay on the road network. Experiments show that Foresight outperforms existing state‑of‑the‑art generative methods.
By Zhenhao Huang, Li Sun, Philip S. Yu
arXiv:2606. 30694v1 Announce Type: cross Abstract: Traffic signal control at urban intersections inherently introduces stop-and-go behavior, resulting in increased delays and reduced traffic efficiency, especially under high traffic demand.
By Qian Hu, Haoyang Peng, Songan Zhang, Ming Yang, Hongtei Eric Tseng
arXiv:2606. 27577v1 Announce Type: cross Abstract: This paper formulates frame-level freeway risk assessment as a multi-agent scene graph-level binary classification problem, where each video or trajectory frame is labeled risky if any TTC- or PET-based conflict violates a specified severity threshold.
By Mahshid Malazizi, Seyedmehdi Khaleghian, Mina Sartipi, Toru Hirano, Yunfei Xu, Hoang H. Nguyen
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