An Automated Machine Learning (AutoML) Method of Risk Prediction for Decision-Making of Autonomous Vehicles
Xiupeng Shi, Yiik Diew Wong, Chen Chai, Michael Z.F. Li
Abstract
This study presents a domain-specific automated machine learning (AutoML) for risk prediction and behaviour assessment, which can be used in the behavioural decision-making and motion trajectory planning of autonomous vehicles (AVs). The AutoML enables end-to-end machine learning from vehicle movement and sensing data to detailed risk levels and corresponding behaviour characteristics, which integrates three main components of: unsupervised risk identification by surrogate risk indicators and big data clustering, feature learning based on XGBoost, and model auto-tuning by Bayesian optimisation. Then, the functions and performance of AutoML are evaluated based on NGSIM data, with assumptions of various sensing configurations or data acquisition conditions. AutoML achieves satisfactory results of behaviour-based risk prediction, which has a predictive power of 91.7% overall accuracy for four risk levels, and about 95% accuracy for safe-risk distinction. Bayesian optimisation guides the self-learning of AutoML to get the optimised feature subsets and hyperparameter values. The identification of key features not only produces better performance with fewer computation costs, but also provides data-driven insights about AV design, such as sensor configurations and sensor data mining, from risk decision-making perspectives. The application potentials of AutoML in AVs are discussed.