This paper proposes AdvGLOW, a framework generating covert adversarial attacks against autonomous driving perception systems. It uses a Glow-based reversible neural network for bi-directional image-latent space transformation, crafting imperceptible perturbations. Coupling layers and actnorm ensure invertible, effective transformations. The method achieves real-time generation (<50 ms). Experiments on driving datasets and models confirm its stealth and effectiveness, pioneering the use of normalizing flows for physically realizable attacks in this context. Repository includes code and configurations.

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.

This paper proposes a lightweight vision and language large model based approach for pedestrian crossing intention prediction.We introduce a two-stage tuning strategy designed to enhance the model’s explicit and implicit reasoning capabilities. In our experiments, we first use Gemini to generate chain-of-thought annotations from the raw data. We then fine-tune a lightweight Qwen 2.5 model on the resulting CoT dataset using the proposed two-stage procedure. Experiments are conducted on two public datasets, PIE and JAAD. This replication package includes all materials required for readers to understand and reproduce the analyses reported in the paper.

• .mat file DLC_acc_FAC_RAC – DLC acceleration test with Front & Rear Active Camber actuators active
• .mat file DLC_acc_FAC_RWS – DLC acceleration test with Front Active Camber & Rear Wheel Steering actuators active
• .mat file DLC_acc_FAC_RAC_RWS – DLC acceleration test with Front & Rear Active Camber and Rear Wheel Steering actuators active
• .mat file PoFF_FAC_RAC – Power-off reaction test with Front & Rear Active Camber actuators active
• .mat file PoFF_FAC_RWS – Power-off reaction test with Front Active Camber & Rear Wheel Steering actuators active
• .mat file PoFF_FAC_RAC_RWS – Power-off reaction test with Front & Rear Active Camber and Rear Wheel Steering actuators active
• Script file DLC_acc – used to generate Figure 8 of the manuscript
• Script file PoFF – used to generate Figure 9 of the manuscript

There are three parts in the replication package:

• Nowcasting

The Nowcasting folder contains the complete pipeline to train the CNN model (Python Code) with images of different vehicle classes and subsequently use the trained model to classify vehicles from video data and estimate their emissions. 

• ODME

The ODME folder includes the software DTALite as well as its input data for estimating OD demand. 

• Simulation

The simulation folder contains the raw demand data extracted from Dynameq initially calibrated by City of Stockholm for the entire Stockholm area and the scripts to transform the demand first into MATSim format and subsequently into SUMO format to realize the hybrid simulation format. Ultimately following this pipeline, the emission for the study area in Kista can be realized for given demand of the current scenario and alternative future scenarios.

To address the limited extensibility of standardized message format this study proposes a modular, edge-intelligent framework — The mobility Operating System (mOS) — integrated with a mixed-reality testbed for realistic validation of infrastructure-guided autonomous vehicle coordination. We analyzed to verify the feasibility of the C-V2X Framework for autonomous vehicle guidance in real-time at the physical testbed. The dataset was collected from the testbed experiment to analyze framework performance (speed profiles, post-encroachment time, and numerical error) and service performance (latency, jitter, and packet loss).

This replication package includes all materials necessary for readers to understand and reproduce the analyses presented in the manuscript. As this study is a review paper that does not involve empirical datasets, experimental code, or algorithmic simulations, the materials provided focus on enabling transparent replication of the literature search and bibliometric procedures used in the study. All literature used in the analysis is fully cited within the review paper, ensuring complete traceability of the sources.

This paper introduces a novel deep learning framework that enhances vehicle re-identification (ReID) accuracy by integrating visual and temporal data. Vehicle ReID, which identifies target vehicles from large volumes of traffic data, is essential for continuous tracking in large-scale monitoring scenarios involving multiple Unmanned Aerial Vehicles (UAVs). UAV-based monitoring, while offering a comprehensive bird’s-eye view (BEV), faces key challenges: loss of uniquely identifiable features and reliance on visual data, which struggles with vehicles of similar appearance. To overcome these issues, our approach incorporates traffic-oriented features based on shockwave theory to model predictable vehicle travel times. Methods have been tested with data from one of the largest drone experiments with 10 drones monitoring 20 intersections for one week in the city of Songdo in Seoul Area. Experimental results demonstrate a 36.8\% improvement in ReID accuracy over traditional methods, highlighting the potential of UAV-based solutions for robust and scalable traffic monitoring.

The replication package containing data and code to reproduce this study' s results contains:
 • code generated or adapted via GPT-based prompt methods,
 • detailed evaluation scripts (Python) and analysis notebooks,
 • extensive persona prompts used in the guided LLM methodology,
 • supplementary hints and strategy documentation for persona modelling, and
 • an extracted subset of the dataset corresponding to the study' s tables and results.

Dataset: MiD2017 Tabellenband Deutschland

This article presents FollowGen, a conditional diffusion model for vehicle trajectory prediction in car-following scenarios. Unlike existing diffusion-based approaches that introduce conditions only during the denoising stage, FollowGen incorporates a scaled noise conditioning mechanism in the forward process to embed historical motion features, and employs a cross-attention transformer in the reverse process to explicitly model interactions between leading and following vehicles. Experiments demonstrate that FollowGen consistently outperforms state-of-the-art baselines, achieving higher accuracy and robustness in diverse car-following environments. 

This repository contains all necessary assets to replicate the study "Multimodal traffic assignment from privacy-protected OD data."

📁 Data

Location: /data
The dataset includes the following components:

• Network Link Data: Parameters (cost, capacity, travel time) for each network link.

• Route Sets: Pre-defined sequences of links (routes) analyzed in the case studies.

💻 Code

Implementation: The full PPTA model, privacy mechanism, and evaluation framework.

• Language: Python 3.8+

• Dependencies: Install required libraries:

  pip install numpy pandas matplotlib scipy cvxpy

  Note: The Mosek solver requires a separate license and installation.

• Execution: Navigate to the PrivacyPreservingTrafficAssignment directory and run:

  python main_ppta_new.py

  This will execute the main script and reproduce the key results from the paper.

📄 Documentation

For a detailed explanation of the methodology, file descriptions, and step-by-step instructions, refer to:
replication-explanatory-file-commtr-PPTA.docx

‘final_df.csv’ includes travel time index (TTI) and all potential  influential factors for pre-lockdown, lockdown, and post-lockdown periods.

This package contains R codes and python code as well as model input data to conduct replication. An explanatory file has also been provided. 

This replication package accompanies the manuscript “What patterns contribute to autonomous vehicle crashes?  A study of Levels 2 and 4 automation using association rule analysis.”
It provides: (i) code to harmonize California DMV AV crash reports and NHTSA SGO crash reports into a unified, analysis-ready table; and (ii) scripts to run Apriori association rule mining with support/confidence/lift threshold

This is the code and datasets for the potential publication "Scalable and Reliable Multi-agent Reinforcement Learning for Traffic Assignment".