The dataset focuses on vehicle conflicts on the motorway, it has both vehicle kinematics data between the ego-vehicle and neighbouring vehicles, as well as traffic-based variables. The traffic variables are collected from 500m road segments and are averaged over a 5 min period before the ego-vehicle passed at every time step. If several consecutive loop detectors were not functioning, the missing cells are stored as -1. The data uses Modified Time-To-Collision as the conflict indicator. It was reconstructed a posteriori by synchronising collected driving data with corresponding traffic data that would theoretically be available in real time. This approach ensures temporal and spatial consistency between datasets, enabling realistic simulation of real-time data collection scenarios.

Replication code for 'Domain-Enhanced Dual-Branch Model for Efficient and Interpretable Accident Anticipation'.

The uploaded files include:

Description file:

Description of the dataset and access to the replication codes.

Datasets

1) Hexagon level datasets used for the clustering analysis in the paper.

2) City-level data used for the main regression analysis.

Code

Code is accessible via `https://github.com/brookefzy/global-city-road-crash`

During the eye-tracking-based data collection —using a Tobii Pro eye camera— participants were presented with a total of nine image pairs depicting streetscapes in their "before" (current) and "after" (following the proliferation of AVs) states for a specific street segment. As a preliminary step, a 60-second calibration procedure was conducted, during which participants were instructed to visually track a moving dot on the screen. Following the briefing and calibration, the image pairs were displayed for data collection, with each pair shown on the screen for 12 seconds.

In terms of the data collected via eye cameras, four important measures can be defined:

• Total Fixation Duration (TFD): the cumulative duration of all fixations within the defined Area of Interest (AOI), reflecting the overall level of visual attention.
• Average Fixation Duration (AFD): the mean duration of individual fixations within an AOI.
• Fixation Count (FC): the total number of fixations recorded within the AOI.

• Time to First Fixation (TTFF): the time elapsed from the onset of the stimulus to the first fixation within the AOI.

Replication code for study of “STFC: Spatio-Temporal Formation Control for Connected and Autonomous Vehicles in Multi-Lane Traffic”.

Please refer to Replication package explanatory file.docx for instructions on running the program and contact information.

This is the package to reproduce the main results of paper "InVDriver: Intra-Instance Aware Vectorized Query-Based Autonomous Driving Transformer" submitted to JICV (Journal of Intelligent and Connected Vehicles).

The data are stored in Excel files, all in tabular format as structured data. Specifically: timetable data (train diagram data), routing data (train service plan data), running time (train travel duration), distance (inter-station distances), line net (network connectivity between lines), and position (actual geographical positions of stations). All aforementioned datasets are sourced from publicly available data provided by Beijing Subway or Baidu Map.

The highD dataset is a new dataset of naturalistic vehicle trajectories recorded on German highways. Using a drone, typical limitations of established traffic data collection methods such as occlusions are overcome by the aerial perspective. Traffic was recorded at six different locations and includes more than 110 500 vehicles. Each vehicle’s trajectory, including vehicle type, size and manoeuvres, is automatically extracted. Using state-of-the-art computer vision algorithms, the positioning error is typically less than ten centimeters. Although the dataset was created for the safety validation of highly automated vehicles, it is also suitable for many other tasks such as the analysis of traffic patterns or the parameterization of driver models.The dataset is extracted from the HighD dataset and pertains to the car type data, presented in the form of a tabular CSV file, which includes parameters such as the speed, acceleration, and longitudinal position of the ego vehicle, as well as the speed, acceleration, and longitudinal position of the preceding vehicle, among others. It can be utilized for studying driver following behavior.

Overview
It is a Python-based application aimed at designing and optimizing air corridors for efficient air traffic management. This project leverages various third-party risks to create safe and efficient air routes.

Features
- Route Optimization
- Safety Analysis
- Data Visualization

Installation
To get started with the Air Corridor Design project, follow these steps:

1. Download the data set.
2. Navigate to the project directory:
    cd ./
3. Install the required dependencies:
    pip install -r requirements.txt

Usage
To run the application, use the following command:
python ./Air_Corridor_Design/main.py

Project Structure
- README.md: This file.
- requirements.txt: List of dependencies required for the project.
- setup.py: Script for completing setup.
- Air_Corridor_Design/: Directory containing the source code.
- Data/: Directory containing data files used by the application.
- Experiments/: Directory containing experimental results.
- Scripts/: Directory containing some scripts for testing.

Contact
For any questions or suggestions, please contact [zhouk23@mails.tsinghua.edu.cn].

The files is the replication for study of "Should Autonomous Vehicles Be Subsidized to Reduce Parking Fees? A Productivity Perspective". The readers can replicate the study using GAMS to solve the non-linear program.

Replication Package for "Compensation scheme and split delivery in a collaborative passenger-parcel transportation system"

Our approach begins with a voxel-based semantic segmentation (SS) pretraining task, designed to explore the semantics and geometry of incomplete regions while acquiring transferable meta-knowledge. Using simulated cooperative perception datasets, we supervise the training of a single vehicle’s perception using the aggregated sensor data from multiple nearby connected autonomous vehicles (CAVs), generating richer and more comprehensive labels. This meta-knowledge is then adapted to the target domain through a dual-phase training strategy—without adding extra model parameters—ensuring efficient deployment. To further enhance the model’s ability to capture long-sequence relationships in 3D voxel grids, we integrate Mamba blocks with deformable convolution and large-kernel attention into the backbone network. Extensive experiments show that MetaSSC achieves state-of-the-art performance, surpassing competing models by a significant margin, while also reducing deployment costs. 

‘NodeSet_2D.mat’ and ‘LinkSet_2D.mat’ describe the node set and link set in Wangjing road network, and ‘ODset.mat’ describe the O-D pairs of demand for UAVs.

This study utilizes datasets from the original baselines, including Los-loop & SZ-taxi (https://github.com/lehaifeng/T-GCN), PEMS-BAY and METR-LA (https://github.com/liyaguang/DCRNN), and PeMSD4 and PeMSD8 (https://github.com/wanhuaiyu/ASTGCN#datasets). The code is available at https://github.com/Chengfeng-Jia/SAFER-Predictor. 

All the materials source coding and data of the Paper titled a multi-agent social interaction model for autonomous vehicle testing

Replication Package for "Towards robust motion control in multi-source uncertain scenarios by robust policy iteration"

This study proposes a Safety Assurance Adaptive Model Predictive Control (SAAMPC) framework to achieve distributed docking/undocking operations for MAVs in uncertain environments. The SAAMPC framework integrates a Model Predictive Control (MPC) controller for trajectory optimization, an adaptive module for dynamic adjustment of control parameters with disturbance, and an adaptive safety assurance module with longitudinal and lateral Control Barrier Functions to ensure safe operation during risky and uncertain conditions. The effectiveness of the proposed approach is validated through simulations in Simulink and field tests on a reduced-scale MAV platform. Experimental results validate that the SAAMPC framework successfully ensures smooth and safe vehicle following and robust execution of docking/undocking operations under uncertainties.

Replication Package for "Enhanced trajectory reconstruction from sparse and noisy GPS data: A progressive chunked transformer approach"

All the materials source coding and results coding  of the Paper titled a trajectory planning and tracking method based on deep hierarchical reinforcement learning