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A Review of Tracking, Prediction and Decision Making Methods for Autonomous Driving
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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.
Forward citations
Cited by 3 Pith papers
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Motion-Grounded Video Reasoning: Understanding and Perceiving Motion at Pixel Level
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A Dynamic Scene Interaction Reasoning Framework for Scene-level Lane-Change Intention and Trajectory Prediction of Multiple Interacting Vehicles
A dynamic graph-attention model jointly predicts every nearby vehicle’s lane-change intention and trajectory, cutting trajectory error by up to ~53% and improving scene coherence on NGSIM and highD.
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SSF-PAN: Semantic Scene Flow-Based Perception for Autonomous Navigation in Traffic Scenarios
An iterative framework that couples LiDAR scene flow estimation with static and dynamic point cloud segmentation reports improved localization and obstacle avoidance in simulated traffic.
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