Review of Decision-Making and Planning Approaches in Automated Driving

Abstract

The number of research papers on decision-making systems in automated driving has increased significantly over the last few years. Decision-making for automated driving can be performed at different levels: (i) strategic level: generating the optimal route up to the destination; (ii) tactical level: identifying and ranking feasible high-level maneuvers that the vehicle can perform, considering the dynamic objects that are in the surroundings; (iii) operational level: generating a collision-free trajectory (path and speed profile) up to the planning horizon; (iv) stability level: computing the motion control commands for tracking the trajectory. Additionally, supervision can be understood as a combination of one or more decision-making levels. Previous reviews have focused either on one of the levels of decision-making or on a specific environment where the approaches are applied, without any distinction between the contexts in which they are applied (robotics, unmanned vehicles or automated driving). This review studies the state-of-the-art on the decision-making approaches applied specifically to automated driving, during the last lustrum.

Behavior Planning for Autonomous Driving: Methodologies, Applications, and Future Orientation

“…The existing methodologies is classified into rule-based, cooperative-based, probabilistic-based, game theoretic-based, and learning-based approaches [19]. One last method of classification and the most recent one is classifying the existing methods into rule-based methods, utility-based methods, probabilistic-based methods, game theory-based methods, learning-based methods, learning-based methods, and cooperativebased methods [20]. After reviewing all these classification approaches, it is decided to adapt to the most general, simple, and comprehensive one that classifies the methodologies into classical approaches and learning approaches as shown in fig.…”

Social Interaction‐Aware Dynamical Models and Decision‐Making for Autonomous Vehicles

“…Utility‐based agents [ 163 ] employ utility functions to guide decision‐making, assigning values to possible world states and selecting actions leading to the highest utility. In contrast to goal‐based agents, which evaluate states based on goal satisfaction, utility‐based agents can handle multiple goals and factor in probability and action cost.…”