Abstract
This paper present three distinct probability-based methods for decision making and trajectory planning layers of overtaking maneuvering functionality for autonomous vehicles. The computation time of the proposed decision-making algorithms may be high, because the number of describing parameters of the traffic situations may vary in a high range. The presented clustering-based, graph-based and dynamic-based methods differ in the complexity of their computation algorithms. Since the decision-making process may require considerable online computation effort, a neural-network-based approach is presented for implementation purposes.
Highlights
Introduction and motivationRecently, the development of the autonomous vehicles has brought new challenges for engineers in the automotive industry
This paper present three distinct probability-based methods for decision making and trajectory planning layers of overtaking maneuvering functionality for autonomous vehicles
3 Graph-based overtaking decision algorithms This algorithm consists of three main layers: the upper layer is responsible for the motion prediction and the mid layer determines the collision free trajectories
Summary
Introduction and motivationRecently, the development of the autonomous vehicles has brought new challenges for engineers in the automotive industry. Abstract This paper present three distinct probability-based methods for decision making and trajectory planning layers of overtaking maneuvering functionality for autonomous vehicles. The role of the paper is to present three own-developed methods for the motion prediction and decision-making process in overtaking maneuvers.
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