120 papers with code • 0 benchmarks • 11 datasets
Self-driving cars : the task of making a car that can drive itself without human guidance.
( Image credit: Learning a Driving Simulator )
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This eliminates the need for human engineers to anticipate what is important in an image and foresee all the necessary rules for safe driving.
Understanding human motion behavior is critical for autonomous moving platforms (like self-driving cars and social robots) if they are to navigate human-centric environments.
We present a generic neural network architecture that uses Composite Fields to detect and construct a spatio-temporal pose which is a single, connected graph whose nodes are the semantic keypoints (e. g., a person's body joints) in multiple frames.
Behavior prediction in dynamic, multi-agent systems is an important problem in the context of self-driving cars, due to the complex representations and interactions of road components, including moving agents (e. g. pedestrians and vehicles) and road context information (e. g. lanes, traffic lights).