Autonomous Vehicle Cost-Prediction-Based Decision-Making Framework For Unavoidable Collisions Using Ethical Foundations

Abstract

Autonomous Vehicles (AVs) hold out the promise of being safer than manually driven cars. However, it is impossible to guarantee the hundred percent avoidance of collisions in a real-life environment with unpredictable objects and events. When accidents become unavoidable, the different reactions of AVs and their outcome will have different consequences. Thus, AVs should incorporate the so-called ‘ethical decision-making algorithm’ when facing unavoidable collisions. This paper is introducing a novel cost-prediction-based decision-making framework incorporating two common ethical foundations human drivers use when facing unavoidable dilemma inducing collisions: Ethical Egoism and Utilitarianism. The cost-prediction algorithm consists of Collision Injury Severity Level Prediction (CISLP) and Cost Evaluation. The CISLP model was trained using both Multinominal Logistic Regression (MLR) and a Decision Tree Classifier (DTC). Both algorithms consider the combination of relationships among traffic collision explanatory features. Four different Cost Evaluation metrics were purposed and compared to suit different application needs. The data set used for training and testing the cost prediction algorithm is the 1999-2017 National Collision Data Base (NCDB) which ensures the realistic and reliability of the algorithm. This paper is a novel paper using Canada's real traffic accident data to propose a cost-prediction-based decision-making framework incorporating different ethical foundations for AVs.