2017 IEEE Intelligent Vehicles Symposium (IV) 2017
DOI: 10.1109/ivs.2017.7995726
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Probabilistic long-term prediction for autonomous vehicles

Abstract: Abstract-Long-term prediction of traffic participants is crucial to enable autonomous driving on public roads. The quality of the prediction directly affects the frequency of trajectory planning. With a poor estimation of the future development, more computational effort has to be put in re-planning, and a safe vehicle state at the end of the planning horizon is not guaranteed. A holistic probabilistic prediction, considering inputs, results and parameters as random variables, highly reduces the problem. A tim… Show more

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Cited by 49 publications
(28 citation statements)
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“…To solve prediction problems for autonomous vehicles, many researchers utilized traditional methods such as constant velocity (CV), constant acceleration (CA), Intelligent Driver Model (IDM), and Kalman Filter (KF) [2]. However, these methods work well only under simple driving scenarios and their performances degrade for long-term prediction as they ignores surrounding context.…”
Section: Interpretable Modelsmentioning
confidence: 99%
“…To solve prediction problems for autonomous vehicles, many researchers utilized traditional methods such as constant velocity (CV), constant acceleration (CA), Intelligent Driver Model (IDM), and Kalman Filter (KF) [2]. However, these methods work well only under simple driving scenarios and their performances degrade for long-term prediction as they ignores surrounding context.…”
Section: Interpretable Modelsmentioning
confidence: 99%
“…E.g. in [10] a particle filter together with an appropriate driver model is applied to estimate the probability distribution of vehicle states. In [11] a nonlinear bicycle model combined with information from a digital map is used within an extended Kalman filter.…”
Section: Probabilistic Forbidden Zonesmentioning
confidence: 99%
“…The features were extracted from the raw trajectory data to generate training samples according to (19)- (20). To In order to demonstrate the advantages of the proposed learning-based model, we compared them with several widely used models in tracking problems: constant velocity model (CVM), constant acceleration model (CAM) and intelligent driver model (IDM).…”
Section: B Case 1: Lane Keepingmentioning
confidence: 99%