Introspection of DNN-based perception functions in automated driving systems : state-of-the-art and open research challenges

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Abstract

Automated driving systems (ADSs) aim to improve the safety, efficiency and comfort of future vehicles. To achieve this, ADSs use sensors to collect raw data from their environment. This data is then processed by a perception subsystem to create semantic knowledge of the world around the vehicle. State-of-the-art ADSs’ perception systems often use deep neural networks for object detection and classification, thanks to their superior performance compared to classical computer vision techniques. However, deep neural network-based perception systems are susceptible to errors, e.g., failing to correctly detect other road users such as pedestrians. For a safety-critical system such as ADS, these errors can result in accidents leading to injury or even death to occupants and road users. Introspection of perception systems in ADS refers to detecting such perception errors to avoid system failures and accidents. Such safety mechanisms are crucial for ensuring the trustworthiness of ADSs. Motivated by the growing importance of the subject in the field of autonomous and automated vehicles, this paper provides a comprehensive review of the techniques that have been proposed in the literature as potential solutions for the introspection of perception errors in ADSs. We classify such techniques based on their main focus, e.g., on object detection, classification and localisation problems. Furthermore, this paper discusses the pros and cons of existing methods while identifying the research gaps and potential future research directions.

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