A Path Prediction Model Based on Multiple Time Series Analysis Tools Used to Detect Unintended Lane Departures
Paper i proceeding, 2020

In this paper, a path prediction model is presented and used to detect unintended lane departures caused by erroneous driving behaviours. The prediction model is inspired by the concept of a linear vector autoregressive model that is commonly used for multiple time series analysis. The original concept is extended to allow sparse historic sampling, which is shown to reduce the computational complexity while maintaining the predictive performance. A real world data set is used to derive and validate the proposed model, for which the performance is benchmarked against a kinematic model. The results show that the proposed model can improve the true-positive rate by 18% and reduce the false-positive rate by 34%, with respect to a constant velocity model and for a prediction horizon of 1.75 s.

Driver Assistance Systems

Automated Vehicle Operation

Advanced Vehicle Safety Systems

Motion Planning



John Dahl

Zenuity AB

Chalmers, Elektroteknik, System- och reglerteknik

AI Sweden

Gabriel Rodrigues de Campos

Zenuity AB

Jonas Fredriksson

Chalmers, Elektroteknik, System- och reglerteknik

IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC

9781728141497 (ISBN)

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)
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Förbättrad säkerhetseffekt av kollisionsundvikande styrande system, del 2.

VINNOVA (2019-05828), 2020-04-01 -- 2020-12-31.





Elektroteknik och elektronik

Robotteknik och automation



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