Safe Autonomous Overtaking Maneuver based on Inter-Vehicular Distance Prediction and Multi-Level Bayesian Decision-Making

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Abstract
This paper describes the design of an overall Multi-Controller Architecture (MCA) for safe automated driving, under uncertainties in highway environment. This MCA combines the decision-making process, the path planning and the control algorithms. In order to ensure the safety and smoothness of the vehicle navigation, a decision-making strategy for handling lane change maneuvers is proposed by means of a robust Two-Sequential Level Bayesian Decision Network (TSLDN). The latter is utilized for the driving situation assessment, the decision-making and for safety verification of the current performed maneuver. Moreover, from actual navigation risk assessment standpoint, a dual-safety criterion combining an Extended Time-To-Collision (ETTC) and a novel Dynamic Predicted Inter-Distance Profile (DPIDP) is developed. The DPIDP is evaluated on-line as the actual distance between vehicles during lane changes maneuvers over an observed control horizon. Thanks to this proposed algorithm, a safety retrospection over the current maneuver risk could be carried out. In this way, the overall MCA described in this paper allows the best probabilistic decision to achieve the vehicle navigation task in hazardous situations while maximizing its safety. Several simulation results show the good performance of the overall proposed control architecture, mainly in terms of efficiency to handle probabilistic decision-making even for very risky/complex scenarios.

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