ISETAuto: Detecting vehicles with depth and radiance information

01/06/2021
by   Zhenyi Liu, et al.
5

Autonomous driving applications use two types of sensor systems to identify vehicles - depth sensing LiDAR and radiance sensing cameras. We compare the performance (average precision) of a ResNet for vehicle detection in complex, daytime, driving scenes when the input is a depth map (D = d(x,y)), a radiance image (L = r(x,y)), or both [D,L]. (1) When the spatial sampling resolution of the depth map and radiance image are equal to typical camera resolutions, a ResNet detects vehicles at higher average precision from depth than radiance. (2) As the spatial sampling of the depth map declines to the range of current LiDAR devices, the ResNet average precision is higher for radiance than depth. (3) For a hybrid system that combines a depth map and radiance image, the average precision is higher than using depth or radiance alone. We established these observations in simulation and then confirmed them using realworld data. The advantage of combining depth and radiance can be explained by noting that the two type of information have complementary weaknesses. The radiance data are limited by dynamic range and motion blur. The LiDAR data have relatively low spatial resolution. The ResNet combines the two data sources effectively to improve overall vehicle detection.

READ FULL TEXT

page 1

page 2

page 3

page 4

page 5

page 7

page 8

page 9

research
04/07/2020

Depth Sensing Beyond LiDAR Range

Depth sensing is a critical component of autonomous driving technologies...
research
12/08/2021

FPPN: Future Pseudo-LiDAR Frame Prediction for Autonomous Driving

LiDAR sensors are widely used in autonomous driving due to the reliable ...
research
01/24/2022

The Vehicle Trajectory Prediction Based on ResNet and EfficientNet Model

At present, a major challenge for the application of automatic driving t...
research
05/24/2023

Detecting disparities in police deployments using dashcam data

Large-scale policing data is vital for detecting inequity in police beha...
research
08/26/2018

Autonomous Driving without a Burden: View from Outside with Elevated LiDAR

The current autonomous driving architecture places a heavy burden in sig...
research
11/20/2020

Robust super-resolution depth imaging via a multi-feature fusion deep network

Three-dimensional imaging plays an important role in imaging application...
research
10/17/2017

Fusion of LiDAR and Camera Sensor Data for Environment Sensing in Driverless Vehicles

Driverless vehicles operate by sensing and perceiving its surrounding en...

Please sign up or login with your details

Forgot password? Click here to reset