Popular GitHub Repositories for Adas

自动驾驶笔记，以解析各模块知识点、整合行业优秀解决方案进行阐述，以帮助自己及有需要的读者；包含深度学习、deeplearning、无人驾驶、BEV、Transformer、ADAS、CVPR、特斯拉AI DAY、大模型、chatgpt等内容.

An open source advanced driver assistance system (ADAS) that uses Jetson Nano as the hardware. Features: Traffic sign detection, Forward collision warning, Lane departure warning.

Stereo vision based object detection for ADAS, autonomous vehicle

Lane and obstacle detection for active assistance during driving. Uses windowed sweep for lane detection. Combination of object tracking and YOLO for obstacles. Determines lane change, relative velocity and time to collision

[TPAMI 2018] Predicting the Driver’s Focus of Attention: the DR(eye)VE Project. A deep neural network learnt to reproduce the human driver focus of attention (FoA) in a variety of real-world driving scenarios.

Simple and Easy simulator YOLO Object Detection with Bird's Eye View

A benchmark towards generalizable reinforcement learning for autonomous driving.

ADAS Car - with Collision Avoidance System (CAS) - on Indian Roads using LIDAR-Camera Low-Level Sensor Fusion. DIY Gadget built with Raspberry Pi, RP LIDAR A1, Pi Cam V2, LED SHIM, NCS 2 and accessories like speaker, power bank etc

A collection of ego-motion estimation projects for AD/ADAS functions

This repository contains the implementation of a lane detection system using the UNet architecture. The model is trained on the BDD100K dataset, leveraging its diverse and large-scale data to ensure robust performance under various weather conditions and different times of day.

This project aims to develope a Road-Segmentation model for Advanced Driver Assistance System (ADAS)

Development of a lane detection and tracking solution for self-driving cars. This is an important part of the autopilot system, to ensure the safety and efficiency of the driver and passengers on the road.

This is an implementation of an adaptive cruise control system based on a computer vision pipeline. This work is based on YOLACT, a State-Of-The-Art real-time instance segmentation network. You're welcome to test and try our code, we hope you'll enjoy this work!

This is my bachelor's thesis, which contains three main features: lane detection, road segmentation, and a Forward Collision Warning (FCW) system

Nvidia End-toEnd deep learning for self driving cars is implemented.

Noise-Adaptive Driving Assistance System (NADAS) using Deep Reinforcement Learning, State-Estimation & State Representation

Driving Assistant System to avoid driver drowsiness powered by generative AI

VDSS - Vehicle Dynamics Safety Simulator

This program uses a deep neural network with several convolutional layers to classify traffic signs. The model is able to recognize traffic signs with an accuracy of 96,2%. It was trained and validated using the German Traffic Sign Dataset with 43 classes (types of traffic signs) and more than 50,000 images in total.

we are building level 2 autonomous systems for self driving car