Logically Sound Arguments for the Effectiveness of ML Safety Measures

Cheng, Chih-Hong; Schuster, Tobias; Burton, Simon

doi:10.1007/978-3-031-14862-0_25

Cited by 4 publications

(1 citation statement)

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“…One intuition can be observed from the fact that, for autonomous driving, pedestrians that are distant should not be equally weighted compared to pedestrians being close by. Another visual example is shown in [156] where the standard IoU metric for bounding box detection will indicate that, within Fig. 6, the prediction in the left image is better than the right.…”

Section: B Performance Evaluation and Safety-aware Metricsmentioning

confidence: 97%

Sensing and Machine Learning for Automotive Perception: A Review

et al. 2023

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Automotive perception involves understanding the external driving environment and the internal state of the vehicle cabin and occupants using sensor data. It is critical to achieving high levels of safety and autonomy in driving. This article provides an overview of different sensor modalities, such as cameras, radars, and light detection and ranging (LiDAR) used commonly for perception, along with the associated data processing techniques. Critical aspects of perception are considered, such as architectures for processing data from single or multiple sensor modalities, sensor data processing algorithms and the role of machine learning techniques, methodologies for validating the performance of perception systems, and safety. The technical challenges for each aspect are analyzed, emphasizing machine learning approaches, given their potential impact on improving perception. Finally, future research opportunities in automotive perception for their wider deployment are outlined.

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Section: B Performance Evaluation and Safety-aware Metricsmentioning

confidence: 97%