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Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

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CLAD: A realistic Continual Learning benchmark for Autonomous Driving.

Eli Verwimp¹, Kuo Yang², Sarah Parisot²

¹PSI, ESAT, KU Leuven, Belgium.

Neural Networks : the Official Journal of the International Neural Network Society

|February 25, 2023

Summary

This summary is machine-generated.

This paper introduces the Continual Learning benchmark for Autonomous Driving (CLAD), addressing object classification and detection challenges. CLAD offers realistic scenarios for advancing continual learning in self-driving systems.

Keywords:

Benchmark Challenge report Classification Continual learning Object detection

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Area of Science:

Computer Vision
Machine Learning
Autonomous Systems

Background:

Existing continual learning benchmarks often represent extreme cases.
There is a need for more realistic benchmarks in autonomous driving.

Approach:

Introduced the Continual Learning benchmark for Autonomous Driving (CLAD).
Utilized the SODA10M dataset for autonomous driving tasks.
Developed CLAD-C for online classification and CLAD-D for continual object detection.

Key Points:

CLAD-C presents both class and domain incremental challenges.
CLAD-D focuses on domain incremental continual object detection.
Analysis of top participant methods from a CLAD-challenge workshop at ICCV 2021.

Conclusions:

Identified inherent difficulties and challenges within the CLAD benchmark.
Proposed pathways for improving continual learning state-of-the-art.
Highlighted promising future research directions in continual learning for autonomous driving.