Autonomous Driving
Showing 36 of 77 projects
A traffic scenario definition and execution engine for the CARLA autonomous driving simulator.
A ROS/ROS2 bridge enabling two-way communication between the CARLA autonomous driving simulator and ROS ecosystems.
A toolkit and dataset for autonomous driving research, including trajectory prediction, 3D LiDAR detection, scene parsing, and video inpainting.
An OpenAI Gym environment wrapper for the CARLA autonomous driving simulator, enabling reinforcement learning research.
A fast and robust ground segmentation algorithm for 3D LiDAR point clouds, using concentric zone-based region-wise processing.
A convolutional neural network model for real-time road-object segmentation from 3D LiDAR point clouds.
A benchmark dataset for long-range (up to 250m) dense depth estimation in autonomous driving, featuring 360° LiDAR ground truth.
A modular autonomous driving platform for developing and testing AV components on CARLA simulator and real-world vehicles.
A robust system for multi-LiDAR extrinsic calibration, real-time odometry, and mapping without manual intervention.
A curated collection of robotics and computer vision datasets for research and development.
A single-stage 3D object detector for point clouds that improves localization precision by explicitly leveraging structure information.
A real-time, uncertainty-aware deep learning model for semantic segmentation of 3D LiDAR point clouds in autonomous driving.
A neural network for object detection using multi-level fusion of camera and radar data, built on Keras RetinaNet.
ROS & ROS2 implementation of Patchwork++, a fast and robust ground segmentation method for 3D LiDAR point clouds.
Utility scripts for loading, visualizing, and inspecting the KITTI-360 autonomous driving dataset.
A lightweight neural network for near-real-time semantic segmentation of LiDAR point clouds using polar coordinate quantization.
A C++/TensorRT inference module for RangeNet++, enabling fast LiDAR semantic segmentation for robotics applications.
A real-time ROS 2 package for detecting drivable roads and sidewalks from LIDAR point clouds in urban autonomous driving scenarios.
A Unity plugin for creating Lanelet2 vector maps for the Autoware autonomous driving platform.
An open-source full-stack ROS-based software for self-driving applications in low-speed urban environments.
A Python devkit for loading, exploring, and manipulating the PandaSet, a large-scale autonomous driving dataset with LiDAR, camera, and annotations.
A Python-based local trajectory planner using multilayer graphs for autonomous race vehicles, returning cost-optimal action sets.
Converts KITTI autonomous driving dataset raw data to ROS bags and provides a C++ library for direct data access.
ROS package for sensor processing, object detection, tracking, and evaluation using the KITTI Vision Benchmark dataset.
A long-term autonomous driving dataset from Europe with multi-sensor data (GPS-RTK, LiDAR, cameras, IMU) for localization and mapping research.
A large-scale driving behavior dataset with LiDAR point clouds, dashboard videos, and sensor data for autonomous driving research.
A deep learning model for joint perception and motion prediction in autonomous driving using bird's eye view maps.
A ROS 2 middleware layer that enables the Eclipse Cyclone DDS implementation for fast, reliable, and robust ROS 2 communication.
A deep learning approach that unifies global place recognition and local 6DoF pose refinement for robust relocalization in large-scale 3D point clouds.
A robust, low-drift, real-time SLAM package for the Livox Horizon LiDAR, designed for highway autonomous driving scenarios.
A Python devkit for working with the Boreas and Boreas Road Trip all-weather autonomous driving datasets.
A comprehensive survey and unified safety framework for embodied AI, covering 400+ papers on risks, attacks, and defenses across perception, cognition, planning, interaction, and agentic systems.
A 3D object detection method that exploits visibility information from LiDAR point clouds to improve accuracy.
A ROS 2 node for real-time LiDAR ground segmentation using a two-phase grid-based algorithm for robotic perception.
A high-precision, grid-based C++ library for ground segmentation in LiDAR point clouds, designed for safety-critical autonomous driving and robotics.
A simple implementation of the DAGGER imitation learning algorithm for autonomous steering control in the Torcs racing simulator.
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