Implementation of paper "Random-Walker Monocular Road Detection in Adverse Conditions Using Automated Spatiotemporal Seed Selection"
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Updated
Dec 18, 2023 - Python
Implementation of paper "Random-Walker Monocular Road Detection in Adverse Conditions Using Automated Spatiotemporal Seed Selection"
This repository is for DDW(Driver Drowsiness Warning Deep Learning Project) using FRCNN with Resnet50 and Custom Backbone in Pytorch=1.10
Visualization tools to be used for the exploration of publicly available Cirrus Dataset from Luminar and Volvo Car
Road segmentation using the Segment Anything Model (SAM)
The Fifth Driver is a project for the Adaptative Computing Developer Contest for Xilinx. We pretend to demonstrate that the Xilinx Ultrascale+ MPSoC architecture is suitable for developing machine learning applications applied to Autonomous Driving.
Version 0.1 of Planned Dashboard for Dashboards
Advanced driver-assistance system on Raspberry Pi using CNN, Python and OpenCV
Centralized E/E Architecture - Extending DES03 with ADAS (Lane Assistance, Autonomes Driving Level 1) & Park-Distance-Control
ADAS for BeamNG.drive
This is a Persian comprehensive review paper of Advanced Driver Assistance Systems (ADAS).
A Visionary-Sign-Detector utilizing Gemini AI for nuanced traffic sign interpretation in ADAS.
A Vanilla LSTM model is used to predict distance to preceding vehicles using data from a Video Logging Tool (VLT)
This repository is for DDW(Driver Drowsiness Warning Computer Vision Project) using Facial Detectors(DLIB)
A Python script that checks driver attentiveness using machine learning computer vision.
Project 1 : ADAS Features on 4-Wheel Drive Smart Car for Raspberry Pi Project 2 : LiDAR data classification using Pretrained Neural Net
OPEN ADAS Lane detection system v1. Implements OpenCV. non learning platforms. Average accuracy and latency.
Time to collision based on lidar and camera data.
This repository can help you to prepare technical questions during the recruiting process for high tech companies. If you are interested on the development of autonomous driving from hardware and software perspective, please take a look to the content of the queries that you will find during the interviews.
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