Mandatory lane changes pose significant challenges to trajectory planning at intersections, where vehicles are required to change lanes mid-block to reach designated turn lanes before the stop bar. MLCs often generate shockwaves that induce increased vehicle delay and fuel consumption, and the presence of human-driven vehicles in mixed traffic further exacerbates this issue. To address these challenges, this study formulates the joint longitudinal-lateral trajectory planning problem in mixed traffic as a multi-agent reinforcement learning task. We propose SS-MA-PPO, a Simulation-Supervised Multi Agent Proximal Policy Optimization framework, which guides connected and automated vehicles in both acceleration and lane-change decisions. A Simulation-Guided Supervisory Module performs offline trajectory rollouts of human-driver models to assess feasibility and safety, and arbitrates online between rule-based and learned policies. The information of surrounding vehicles is incorporated in the observation to achieve vehicle cooperation, and a transfer learning mechanism is designed to accelerate training.

This replication package contains the dataset (CTSU) and source code used in TrafficPerceiver.

All the materials source coding and results coding of  Vehicle-Infrastructure Cooperative General Object Detection through Feature Flow and Differentiable Pose-based Spatial Alignment

Accurate short-horizon trajectory prediction is crucial for safe and reliable autonomous driving. However, existing vision-language models (VLMs) often fail to accurately understand driving scenes and generate trustworthy trajectories. To address this challenge, this paper introduces KEPT, a knowledge-enhanced VLM framework that predicts ego trajectories directly from consecutive front-view driving frames. KEPT integrates a temporal frequency–spatial fusion (TFSF) video encoder, which is trained via self-supervised learning with hard-negative mining, with a k-means & HNSW retrieval-augmented generation (RAG) pipeline. Retrieved prior knowledge is added into chain-of-thought (CoT) prompts with explicit planning constraints, while a triple-stage fine-tuning paradigm aligns the VLM backbone to enhance spatial perception and trajectory prediction capabilities. This replication package includes all materials required for readers to understand and reproduce the analyses reported in the paper.

This paper introduces latency-aware trajectory prediction, a new task that explicitly accounts for latency and repurposes it as a useful signal. We present LatenAux, a consolidated auxiliary learning paradigm that first decouples prediction into two tasks: a primary task that predicts valid-horizon trajectories from historical data, and an auxiliary task that utilizes latency-inclusive observations. By allowing the auxiliary branch access to latency-crafted inputs, LatenAux is then committed to transferring latency-aware knowledge to the primary branch via a progressive feature alignment strategy. Extensive experiments on two large-scale real-world datasets demonstrate the effectiveness and superiority of LatenAux, showing that it consistently supports latency-aware modeling and delivers more accurate and reliable trajectory forecasts.