mypostw

nmeapower

gprmc & power

apollo

An open autonomous driving platform

caffe-yolo

Caffe for YOLO

Object-detection-in-Point-Cloud-road-boundary

Object detection in Point Cloud is popular in HD Map and sensor-based autonomous driving. There basically four types of object you can obtain in daily scenario: road surface - contains painted lane marking and pavement area, support facility - contains road boundary (guardrail and curb), road sign, light pole, etc., uncorrelated object - for example, sidewalk, building, etc., and moving object - such like pedestrian, vehicle, bicycle, etc. In this project, please search references, design and prototype your road boundary (guardrail) detection algorithm.

RobotRacing2018

This repository contains the current competition code (2018). This code will be used for the IARRC 2018. This code currently handles LIDAR mapping, trajectory rollout, PID speed control, lane detection and traffic light detection.

ethzasl_msf

MSF - Modular framework for multi sensor fusion based on an Extended Kalman Filter (EKF)

kalibr

The Kalibr calibration toolbox

RoadDamageDetector

RoadDamageDetector-1

Implementation of a road damage detector in Chainer

ethzasl_apriltag2

Catkinized version of the AprilTag C++ implementation

nvcaffe-yolov2

Custom distribution of Caffe

yolov2-caffe-inference

darknet-cpp

C++ version of darknet (Yolo library)

YoloDarknetSharp

Basic C# Yolo Wrapper (C++ Bridge included)

Caffe-YOLOv2-Windows

YoloV2NCS

This project shows how to run tiny yolo v2 with movidius stick.

neural-road-inspector

After a hurricane, roads are often flooded or washed out, making them treacherous for motorists. Using state of the art deep learning methods, I attempted to automatically annotate flooded, washed out, or otherwise severely damaged roads. My goal is create a tool that can help detect and visualize anomalous roads in a simple user interface.

VPGNet

VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition (ICCV 2017)

caffe-yolov2

study

PowerLaneFinding-ubuntu-

YOLO-TensorRT-GIE-

This code is an implementation of a trained YOLO neural network used with the TensorRT framework.

arrow_road_marking_detection

road-curve-detection

Given a 3D point cloud obtained by a LiDAR sensor mounted on a car, this project goal is to output the road properties.