Mono Video-Based AI Corridor for Model-Free Detection of Collision-Relevant Obstacles

04/24/2023
by   Thomas Michalke, et al.
0

The detection of previously unseen, unexpected obstacles on the road is a major challenge for automated driving systems. Different from the detection of ordinary objects with pre-definable classes, detecting unexpected obstacles on the road cannot be resolved by upscaling the sensor technology alone (e.g., high resolution video imagers / radar antennas, denser LiDAR scan lines). This is due to the fact, that there is a wide variety in the types of unexpected obstacles that also do not share a common appearance (e.g., lost cargo as a suitcase or bicycle, tire fragments, a tree stem). Also adding object classes or adding all of these objects to a common unexpected obstacle class does not scale. In this contribution, we study the feasibility of using a deep learning video-based lane corridor (called AI ego-corridor) to ease the challenge by inverting the problem: Instead of detecting a previously unseen object, the AI ego-corridor detects that the ego-lane ahead ends. A smart ground-truth definition enables an easy feature-based classification of an abrupt end of the ego-lane. We propose two neural network designs and research among other things the potential of training with synthetic data. We evaluate our approach on a test vehicle platform. It is shown that the approach is able to detect numerous previously unseen obstacles at a distance of up to 300 m with a detection rate of 95

READ FULL TEXT

page 1

page 2

page 4

page 5

page 6

page 7

page 8

research
10/21/2021

Mixer-based lidar lane detection network and dataset for urban roads

Accurate lane detection under various road conditions is a critical func...
research
09/29/2022

NVRadarNet: Real-Time Radar Obstacle and Free Space Detection for Autonomous Driving

Detecting obstacles is crucial for safe and efficient autonomous driving...
research
12/20/2016

Detecting Unexpected Obstacles for Self-Driving Cars: Fusing Deep Learning and Geometric Modeling

The detection of small road hazards, such as lost cargo, is a vital capa...
research
09/11/2020

The PREVENTION Challenge: How Good Are Humans Predicting Lane Changes?

While driving on highways, every driver tries to be aware of the behavio...
research
09/15/2016

Lost and Found: Detecting Small Road Hazards for Self-Driving Vehicles

Detecting small obstacles on the road ahead is a critical part of the dr...
research
05/31/2019

Driver Behavior Analysis Using Lane Departure Detection Under Challenging Conditions

In this paper, we present a novel model to detect lane regions and extra...
research
09/08/2023

Have We Ever Encountered This Before? Retrieving Out-of-Distribution Road Obstacles from Driving Scenes

In the life cycle of highly automated systems operating in an open and d...

Please sign up or login with your details

Forgot password? Click here to reset