Learn to map surrounding vehicles onto a bird's eye view of the scene.

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.

Lane identification system for camera based systems.

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

Automatic Parking is an autonomous car maneuvering system (part of ADAS) that moves a vehicle from a traffic lane into a parking spot to perform parallel parking. The automatic parking system aims to enhance the comfort and safety of driving in constrained environments where much attention and experience is required to steer the car. The parking maneuver is achieved by means of coordinated control of the steering angle and speed which considers the actual situation i.e., the free spaces and the obstacle spaces in the environment to ensure collision-free motion within the available space. The path shape required for a parking maneuver is evaluated from the environmental model, generating a fifth-order polynomial, the corresponding control commands are selected and parameterized to provide motion within the available space. In real-time application, the commands are executed by the car servo-systems which drive the vehicle into the parking place.

Graduation project repository, Real-time vehicle detection using two different approaches. HOG+SVM traditional approach and Deep Learning based approach using state of the art YOLO convolutional neural network.

Vehicle Detection + Advanced Lane Finding for ADAS

This repo includes Unet, Spatial CNN (S-CNN) and VPNet for lane segmentation, and YOLO, Faster-RCNN, Stereo-RCNN for vehicle detection.

Lane depertaure and Yolo objection detection C++ Linux

[1 FPS / CPU only] OpenVINO+ADAS+LattePandaAlpha. CPU / GPU / NCS. RealTime semantic-segmentaion. Python3.5+OpenCV3.4.3+PIL

Implementation of A Random Finite Set Approach for Dynamic Occupancy Grid Maps with Real-Time Application

A project to demonstrate lane detection with a front facing camera.

Semi-automatic detection, tracking and labelling of active targets for autonomous driving.

Eye state localisation and detection for use in Advanced Driver Assistance System.

An embedded OBU which makes vehicles programmable for everyone.

Functional safety (ISO-26262) and ADAS

Archives for undergraduate minor

Client example of Carla simulator with OpenCV in python

ADAS made trivial

Detection of vehicles using Image processing.

Gra planszowa - Kwarantanna

Lane Detection System using OpenCV and Python

A vehicle-pedestrian interaction framework for simulation.

Collision Avoidance System for Self-Driving Vehicles by Delta Autonomy, Robotics Institute, CMU

Search-based software testing of a pedestrian detection system using ESI Pro-SiVIC

Detect lane lines on the road with advanced computer vision techniques

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

Lane detection using matlab toolbox

자율주행차에서 차선인식을 위한 이론 및 코드 실습 + OpenCV 코드 실습