Building UIUC's 1st Self-driving Car from Scratch
Online monitoring for safe pedestrian-vehicle interactions
Peter Du, Zhe Huang, Tianqi Liu, Ke Xu, Qichao Gao, Hussein Sibai, Katherine Driggs-Campbell, Sayan Mitra
Accepted to IEEE Intelligent Transportation Systems Society Conference 2020 (equal contribution for author 2-6 )
Object Detection & Tracking & Position Estimation
Overview
Adapt YOLOv3 algorithm to detect vehicle, pedestrian, stop sign, and traffic light
As a quick and efficient implementation, I use Triangle Similarity to estimate vehicle and pedestrian distances (y positions)
Use meter-to-pixel ratio to estimate x positions
Use SORT, an online Kalman Filter based algorithm, for tracking
Public Road Test Demo Video
label format: <class label>-<tracking ID> [x, y]
x coordinates: negative means to the left of ego-vehicle; positive means to the right of ego-vehicle
y coordinates: the depth from ego-vehicle
Automatic Braking: Detection & Tracking & Control
Pedestrian Detection and Tracking
Adapt YOLOv3 algorithm to detect the pedestrian
Use SORT, an online Kalman Filter based algorithm, for tracking
Controller
Simple PID controller as a quick start
Indoor Arena Demo Video
Cruise Control: Lane Detection & Controller
Lane Detection
Our first implementation utilizes Perspective Transformation, Color Masks, Sobel Filter, Edge Detection, and Morphological Closure
Switch to LaneNet for better performances
Controller
Simple PID controller as a quick start
Indoor Arena Demo Video
System Overview
Test Vehicle Platform
We are fortunately to play with UIUC's very 1st self-driving car
Polaris GEM e2 from AutonomouStuff
PACMOD: drive-by-wire lower level CAN control
Joystick Controller
Sensors
Velodyne VLP-16 LiDAR
Delphi ESR 2.5 Radar
GPS & IMU
Mako G-319C color camera, 1920x1440
Networking
ROS, Ethernet, CAN, USB
Onboard PC
6 Generation Intel® CoreTM i7-6700 quad-core, 2.4 GHz
NVIDIA® GeForce® GTX 1050 GPU (2GB GDDR5)