Recognition of Lane Change Intentions Fusing Features of Driving Situation, Driver Behavior, and Vehicle Movement by Means of Neural Networks

Leonhardt, Veit; Wanielik, Gerd

doi:10.1007/978-3-319-66972-4_6

Cited by 12 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In practical driving, drivers usually predict the behavior of vehicles in the adjacent lane before modulating the driving strategy of the host vehicle [6]. Prediction of lane change maneuvers intended by the driver is solved by an artificial neural network with fusing features modeling the environmental situation [14]. The characterization of vehicles' behavior based on their velocities can be modelled by information theory [1].…”

Section: A Transport Analysismentioning

confidence: 99%

Determining Hybrid Re-id Features of Vehicles in Videos for Transport Analysis

David¹,

Borsodi²

2022

Infocommunications journal

View full text Add to dashboard Cite

The research topic presented in this paper belongs to computer vision problems in the transport application area, where the statistical data of the results give the input for the transport analysis. Although object tracking in a controlled environment could be performed with good results in general, accurate and detailed annotation of vehicles is a common problem in traffic analysis. Such annotation includes static and dynamic attributes of numerous vehicles. Most recent object trackers employ CNNs to compute the so-called re-identification features of the bounding boxes. In this paper we introduce hybrid re-identification features, which combine latent, static, and dynamic attributes to improve tracking. Furthermore, we propose a lightweight solution that could be integgrated in a real-time multi-camera tracking system.

show abstract

Section: A Transport Analysismentioning

confidence: 99%