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Explainable Machine Learning for Autonomous Vehicle Positioning Using SHAP

Onyekpe, Uche ORCID: https://orcid.org/0000-0001-8033-9394, Lu, Yang ORCID: https://orcid.org/0000-0002-0583-2688, Apostolopoulou, Eleni, Palade, Vasile, Eyo, Eyo Umo and Kanarachos, Stratis (2022) Explainable Machine Learning for Autonomous Vehicle Positioning Using SHAP. In: Mehta, M., Palade, V. and Chatterjee, I., (eds.) Explainable AI: Foundations, Methodologies and Applications. Intelligent Systems Reference Library, 232 (232). Springer, pp. 157-183

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Despite the recent advancements in Autonomous Vehicle (AV) technology, safety still remains a key challenge for their commercialisation and development. One of the major systems influencing the safety of AVs is its navigation system. Road localisation of autonomous vehicles is reliant on consistent accurate Global Navigation Satellite System (GNSS) positioning information. The GNSS relies on a number of satellites to perform triangulation and may experience signal loss around tall buildings, bridges, tunnels, trees, etc. We previously proposed the Wheel Odometry Neural Network (WhONet) as an approach to provide continuous positioning information in the absence of the GNSS signals. We achieved this by integrating the GNSS output with the wheel encoders’ measurements from the vehicle whilst also learning the uncertainties present in the position estimation. However, the positioning problem is a safety critical one and thus requires a qualitative assessment of the reasons for the predictions of the WhONet model at any point of use. There is therefore the need to provide explanations for the WhONet’s predictions to justify its reliability and thus provide a higher level of transparency and accountability to relevant stakeholders. Explainability in this work is achieved through the use of Shapley Additive exPlanations (SHAP) to examine the decision-making process of the WhONet model on an Inertial and Odometry Vehicle Navigation Benchmark Data subset describing an approximate straight-line trajectory. Our study shows that on an approximate straight-line motion, the two rear wheels are responsible for the most increase in the position uncertainty estimation error compared to the two front wheels.

Item Type: Book Section
Status: Published
DOI: https://doi.org/10.1007/978-3-031-12807-3_8
School/Department: School of Science, Technology and Health
URI: https://ray.yorksj.ac.uk/id/eprint/7079

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