Autonomous Driving
Showing 36 of 72 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.
An OpenAI Gym environment wrapper for the CARLA autonomous driving simulator, enabling reinforcement learning research.
A toolkit and dataset for autonomous driving research, including trajectory prediction, 3D LiDAR detection, scene parsing, and video inpainting.
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.
Utility scripts for loading, visualizing, and inspecting the KITTI-360 autonomous driving dataset.
ROS & ROS2 implementation of Patchwork++, a fast and robust ground segmentation method for 3D LiDAR point clouds.
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 3D object detection method that exploits visibility information from LiDAR point clouds to improve accuracy.
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 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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