Title
A probabilistic long term prediction approach for highway scenarios
Abstract
Risk estimation for the current traffic situation is crucial for safe autonomous driving systems. The computation of risk estimates however is always uncertain, especially if the behavior of traffic participants has to be taken into account. Besides risk estimation, knowledge about the future behavior of other traffic participants can be used for Adaptive Cruise Control Applications, helping to choose a driving strategy with more foresight, which is not only desirable under comfort aspects, but can also be used to reduce fuel consumption. In this publication we focus on highway scenarios, where possible behaviors consist of changes in acceleration and lane-change maneuvers. Based on this insight we present a novel approach for the prediction of future positions in highway scenarios.
Year
DOI
Venue
2014
10.1109/ITSC.2014.6957776
ITSC
Keywords
Field
DocType
gaussian distribution,gaussian processes,automobiles,intelligent transportation systems,mixture models,road traffic control,gaussian mixtures,comfort aspects,conditional distribution,constant acceleration assumption,constant velocity assumption,fuel consumption reduction,highway scenarios,lane-change maneuvers,position uncertainty prediction,position uncertainty quantification,probabilistic long-term prediction approach,safe autonomous driving systems,situation dependent probabilistic output,traffic dependent predictions,traffic participant behavior,traffic situation,uncertain risk estimation computation,behavior,risk assessment
Long-term prediction,Simulation,Cruise control,Risk assessment,Futures studies,Acceleration,Probabilistic logic,Engineering,Fuel efficiency,Computation
Conference
Citations 
PageRank 
References 
2
0.41
7
Authors
4
Name
Order
Citations
PageRank
Schlechtriemen, J.191.32
Wedel, A.220.41
Breuel, G.3341.87
Klaus-Dieter Kuhnert431.46