Predicting the speed of a vehicle for a future point on the road ahead is an important subtask of many advanced safety systems. We propose a two-stage neural net approach: first, (a small number of) characteristics of the overall speed distribution at a given location are estimated from road features alone. Second, for the case of a particular trip the speed at the current location, together with the speed characteristics output by the first stage for both the current and a future location, is used to predict the speed at the latter. Our approach parallels the previous empirical constant-percentile approach. It achieves nearly the same predictive accuracy, while at the same time reduces the data requirement to a feasible amount and additionally is able to generalize to extreme speeds not previously seen in the training set.
Predicting driving speed using neural networks
01.01.2003
407225 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Predicting Driving Speed Using Neural Networks
British Library Conference Proceedings | 2003
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