The is the code for paper "Towards fair lights: A multi-agent masked deep reinforcement learning for efficient corridor-level traffic signal control". Run train.py for deep reinforcement learning model training. Run test.py for testing.

The code is used in the paper:"Beyond Conventional Vision: RGB-Event Fusion for Robust Object Detection in Dynamic Traffic Scenarios"

This replication package includes the complete code and dataset used in our study titled “Can Combined Virtual-Real Testing Speed Up Autonomous Vehicle Testing? Findings from AEB Field Experiments." The materials provided are essential for researchers and practitioners interested in replicating our experiments and validating the findings.

该数据集包括 2023 年 10 月城市十字路口四个方向的监控视频。 这些视频的分辨率为 3840×2160，帧速率为每秒 30 帧 （fps），场景理解标注涵盖交通拥堵程度、行人和车辆数量、行为意图等正常场景。

This repository includes the replication code for utilizing the PFD method for traffic state estimation applications.

The replication package contains the matlab code and data to replicate the performed experiments described in the manuscript.

This dataset is the original data used in the paper Privacy-preserving Personalized Pricing and Matching for Ride-hailing Platforms. Based on the real demand information contained in this dataset, we generated the dataset used in our study. The generation process is detailed in the numerical experiments section of the paper.

Safety-critical scenario test for intelligent vehicles via hybrid participations of natural and adversarial agents

This repository contains the official code and datasets for A Knowledge-Informed Deep Learning Paradigm for Generalizable and Stability-Optimized Car-Following Models.

This project implements a hybrid clustering approach combining DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and Natural Breaks (Jenks) to classify traffic states. The algorithm is designed to extract meaningful traffic patterns from speed data across road segments by determining relative speed thresholds for various traffic states and analyzing the inter-segment relationships in traffic behavior.

The main functions include:

• Determining relative speed thresholds for traffic state classification using hybrid clustering.
• Computing statistical metrics (mean, standard deviation, etc.) of relative speeds in each cluster.
• Analyzing the correlation of traffic states between adjacent road segments, including:
  • Linear regression slope and intercept.
  • Pearson correlation coefficient (R).
  • p-value of statistical significance.
  • Standard error of regression coefficients.

The deviation of the FPS of adjacent vehicles will significantly reduce the CPBA of the target vehicle, and may even render it infeasible for passages to board or alight the vehicle. To mitigate this negative impact, this paper proposes a BHA* path planning method for non-standard FPS. In this method, the FPS optimization strikes a balance between the safety distance and the allowed OAVD to enhance the CPBA. Then, the BHA* algorithm rapidly generate paths for optimized FPS without FPS planning errors. The total time for FPS optimization and path planning is within 2.5 seconds, which meets the real - time requirements of practical applications. Simulation experiments based on the parameters of real vehicles and parking scenarios, as well as comparative experiments with the HA* algorithm, were conducted to verify the effectiveness and superiority of the proposed method. This research pioneers the integration of considerations regarding the CPBA into automatic parking path planning. This database contains the implementation code and supporting data for the proposed method

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.