Neurosymbolic hybrid approach to driver collision warning

Kyongsik Yun; Thomas Lu; Alexander Huyen; Patrick Hammer; Pei Wang

doi:10.1117/12.2620209

27 May 2022 Neurosymbolic hybrid approach to driver collision warning

Kyongsik Yun, Thomas Lu, Alexander Huyen, Patrick Hammer, Pei Wang

Proceedings Volume 12101, Pattern Recognition and Tracking XXXIII; 121010G (2022) https://doi.org/10.1117/12.2620209
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Deep learning alone has achieved state-of-the-art results in many areas, from complex gameplay to predicting protein structures. In particular, in image classification and recognition, deep learning models have achieved much higher accuracy than humans. But sometimes it can be very difficult to debug if the deep learning model doesn't work. Deep learning models can be vulnerable and are very sensitive to changes in data distribution. Here, we combine deep learning-based object recognition and tracking with an adaptive neurosymbolic network agent, called the non-axiomatic reasoning system, that can adapt to its environment by building a concept based on perceptual sequences. We achieved an improved intersection-over-union (IOU) object recognition performance of 0.65 in the adaptive retraining model compared to IOU 0.31 in the COCO data pre-trained model. We improved the object detection limits using RADAR sensors in a simulated environment.

Conference Presentation

Citation Download Citation

Kyongsik Yun, Thomas Lu, Alexander Huyen, Patrick Hammer, and Pei Wang "Neurosymbolic hybrid approach to driver collision warning", Proc. SPIE 12101, Pattern Recognition and Tracking XXXIII, 121010G (27 May 2022); https://doi.org/10.1117/12.2620209

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