adas

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.

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

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

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.

Lane identification system for camera based systems.

A powerful open environment for automotive bus monitoring, simulation, testing, diagnostics, calibration and so on. It supports all kinds of mainstream hardware such as TOSUN, Vector, PEAK, Kvaser, Intrepidcs, ZLG, CANable, CandleLight, cantact and so on. It is a permanent free software for all automotive engineers.

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

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.

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

Vehicle Detection + Advanced Lane Finding for ADAS

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.

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

Deep learning based Gaze detection model to control the mouse pointer of your computer

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

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

A vehicle-pedestrian interaction framework for simulation.

TSMaster Demo Program Source Code

Implementation of the Automatic Emergency Braking System using deep learning.

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

Functional safety (ISO-26262) and ADAS

Algorithm-Verification & Scenario-Visualization-Kit(ASK): The project(ASK) aims to develop a ROS-free tool for SW solution validation and Algorithm reliability verification, which can be used in multi-domain, e.g. Robot, ADAS, APA, AVP, etc.

Driver Monitoring System by using deep learning model Gaze, Face detection, Face Landmark, and Head pose estimation.

YIPSO, A Novel Autonomous Parking System

An embedded OBU which makes vehicles programmable for everyone.

Client example of Carla simulator with OpenCV in python

My work during the research internship "Automotive Control using Surface Electromyography" at The University of Tokyo, Jun.-Sep. 2020

Driver eye and hand tracker