Motion (physics)

Gene

Algorithm

Engineering

Transport engineering

Ecology

Pure mathematics

Biology

Field (mathematics)

ENCODE

Biochemistry

Computer science

Software

Mathematics

Holism

Artificial intelligence

Chemistry

Programming language

Intelligent transportation system

Machine learning

State (computer science)

IEEE Transactions on Intelligent Transportation Systems

Scenario understanding and motion prediction are essential components for completely replacing human drivers and for enabling highly and fully automated driving (SAE-Level 4/5). In deeply stochastic and uncertain traffic scenarios, autonomous driving software must act beyond existing traffic rules and must predict critical situations in advance to provide safe and comfortable rides. In addition, comprehensive prediction models intend not just to reproduce, but rather to encode the human driver behavior, which requires profound scenario understanding. Hence, research in the field of scenario understanding and motion prediction also contributes to enable intelligent driver behavior models in general. This paper aims to review the state of research and outline common methods. A classification of these models is proposed according to their underlying investigation methodology. Based on this classification, a comparison is drawn between three specific prediction methods, which considers specific functional aspects and general requirements of applicability. The results of the comparison reveal a trade-off between holism and explainability in the state of the art. In conclusion, suggestions for future research objectives to solve this conflict are proposed.

Scenario Understanding and Motion Prediction for Autonomous Vehicles—Review and Comparison