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Teaching Vehicles to Anticipate: A Systematic Study on Probabilistic Behavior Prediction Using Large Data Sets

Wirthmueller, Florian and Schlechtriemen, Julian and Hipp, Jochen and Reichert, Manfred (2021) Teaching Vehicles to Anticipate: A Systematic Study on Probabilistic Behavior Prediction Using Large Data Sets. IEEE Transactions on Intelligent Transportation Systems . (Accepted for Publication)

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Official URL: https://ieeexplore.ieee.org/document/9126120

Abstract

By observing their environment as well as other traffic participants, humans are enabled to drive road vehicles safely. Vehicle passengers, however, perceive a notable difference between non-experienced and experienced drivers. In particular, they may get the impression that the latter ones anticipate what will happen in the next few moments and consider these foresights in their driving behavior. To make the driving style of automated vehicles comparable to the one of human drivers with respect to comfort and perceived safety, the aforementioned anticipation skills need to become a built-in feature of self-driving vehicles. This article provides a systematic comparison of methods and strategies to generate this intention for self-driving cars using machine learning techniques. To implement and test these algorithms we use a large data set collected over more than 30,000 km of highway driving and containing approximately 40 000 real-world driving situations. We further show that it is possible to classify driving maneuvers upcoming within the next 5 s with an Area Under the ROC Curve (AUC) above 0.92 for all defined maneuver classes. This enables us to predict the lateral position with a prediction horizon of 5 s with a median lateral error of less than 0.21 m.

Item Type:Article
Subjects:DBIS Research > Publications
ID Code:2005
Deposited By: Prof. Dr. Manfred Reichert
BibTex Export:BibTeX
Deposited On:18 May 2021 09:12
Last Modified:18 May 2021 09:12

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