Modeling and Quantitative Assessment of Fundamental Environment Complexity for Autonomous and Electric Vehicles

Current challenges for autonomous and electric vehicle (AEV) technology derives from the complex road traffic environments and the requirement of efficient methods for their modeling and quantitative assessment. A review of literature indicates that the development of AEV technologies, i.e., environmental perception, path planning and tracking control, modeling and quantitative assessment of environment complexity are exceedingly little. In this research, in view of information theory, a hierarchical model is designed to explicitly consider the fundamental environment complexity (FEC) and based on it, a novel quantitative assessment approach is proposed. In particular, the amount of information is calculated from the probability of each information source together with the identified weighting factors of category elements and the information coefficients of environment complexity. Finally, the effectiveness of the proposed FEC model and quantitative assessment method is examined with a practical analysis over two sections of traffic road.