Explainable Artificial Intelligence based Classification of Automotive Radar Targets
- Resource Type
- Conference
- Authors
- Pandey, Neeraj; Ram, Shobha Sundar
- Source
- 2023 IEEE Radar Conference (RadarConf23) Radar Conference (RadarConf23), 2023 IEEE. :1-6 May, 2023
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Fields, Waves and Electromagnetics
Robotics and Control Systems
Signal Processing and Analysis
Perturbation methods
Decision making
Wheels
Radar imaging
Generative adversarial networks
Automobiles
Inverse synthetic aperture radar
inverse synthetic aperture radar
radar imaging
explainable artificial intelligence
automotive radar
counterfactual explanations
- Language
Explainable decision-making is a key component for compliance with regulatory frameworks and winning trust among end users. In this work, we propose to understand the mis-classification of automotive radar images through counterfactual explanations obtained from generative adversarial networks. The proposed method enables perturbations of original radar images belonging to a query class to result in counterfactual images that are classified as the distractor class. The key requirement is that the perturbations must result in realistic images that belong to the original distribution of the query class and also provide physics-based insights into the causes of the misclassification. We test the methods on simulated automotive inverse synthetic aperture radar data images for a query class of a four-wheel mid-size car and a distractor class of a three-wheel auto-rickshaw. Our results show that the shadowing of one or more wheels of the query class is most likely to result in misclassification.