A Review of Tracking, Prediction and Decision Making Methods for Autonomous Driving
It synthesizes existing research for developers and researchers in autonomous driving, but is incremental as it reviews prior work without novel contributions.
This literature review examines tracking, prediction, and decision-making methods for autonomous driving, covering neural networks, stochastic techniques, and deep reinforcement learning, but does not present new results or concrete numbers.
This literature review focuses on three important aspects of an autonomous car system: tracking (assessing the identity of the actors such as cars, pedestrians or obstacles in a sequence of observations), prediction (predicting the future motion of surrounding vehicles in order to navigate through various traffic scenarios) and decision making (analyzing the available actions of the ego car and their consequences to the entire driving context). For tracking and prediction, approaches based on (deep) neural networks and other, especially stochastic techniques, are reported. For decision making, deep reinforcement learning algorithms are presented, together with methods used to explore different alternative actions, such as Monte Carlo Tree Search.