Category
Data type
Tag
Region
Domain-Enhanced Dual-Branch Model for Efficient and Interpretable Accident Anticipation (code)
Replication code for 'Domain-Enhanced Dual-Branch Model for Efficient and Interpretable Accident Anticipation'.

Urban Visual Clusters and Road Transport Fatalities: A Global City-level Image Analysis
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`

Young urban dwellers' acceptance of autonomous vehicle-induced landscape changes: an eye-tracking study - dataset to JICV manuscript
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.

STFC: Spatio-Temporal Formation Control for Connected and Autonomous Vehicles in Multi-Lane Traffic
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.

InVDriver: Intra-Instance Aware Vectorized Query-Based Autonomous Driving Transformer
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).

Interaction Dataset of Autonomous Vehicles with Traffic Lights and Signs

Replication package for Urba n rail transit resilience under different operation schemes: A percolation -based approach
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 dataset on the following behaviors of drivers on highways.
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.

Project: A Risk-based Unmanned Aerial Vehicle Path Planning Scheme for Complex Air-Ground Environments
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].





Replication for "Should Autonomous Vehicles Be Subsidized to Reduce Parking Fees? A Productivity Perspective"
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.

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

MetaSSC: Enhancing 3D Semantic Scene Completion for Autonomous Driving through Meta-Learning and Long-sequence Modeling
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.

Road Network Data in Wangjing District, Beijing, China
‘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.

SAFER-Predictor
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.

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

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

Safety Assurance Adaptive Control for Modular Autonomous Vehicles
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.

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

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

Replication package of Enhancing Driver Emotion Recognition Through Deep Ensemble Classification.
All the materials source coding and results coding of Enhancing Driver Emotion Recognition Through Deep Ensemble Classification.
