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    • 5. 发明公开
    • PEER VEHICLE BEHAVIOR PREDICTION SYSTEM AND METHOD
    • EP3937081A1
    • 2022-01-12
    • EP20184495.8
    • 2020-07-07
    • KNORR-BREMSE Systeme für Nutzfahrzeuge GmbH
    • KARZ, Gergely JakabBoKA, JenoDUDAS, ZsoltGYENIS, TamásLINDENMAIER, LaszloNEMETH, HubaLÓRÀNT, SzabóSZAPPANOS, AndrasSZÖLLOSI, AdamVÖRÖS, DánielGYURKÓ, Zoltán
    • G06K9/62G06K9/00
    • Peer vehicle behaviour prediction can be based on tail light recognition of that vehicle since intended behaviour of the peer vehicle is usually announced by a human driver or an automated driving system by activating tail lights correspondingly. In autonomous vehicle systems, there are multiple sensors that map the environment of the autonomous vehicle. These sensors include LiDARs, cameras, thermal cameras, ultrasonic sensors or others. Optical cameras provide a video stream of the environment. Vehicle tail light recognition can be treated as an application of video acquisition and thus is susceptible to spatial and temporal image recognition. Such spatial and temporal image recognition is susceptible to deep learning approaches. Disclosed is a control system for autonomous driving of a vehicle, comprising a sensor that is configured to detect changes in the illumination state of at least a first kind of indicator lights and a second kind of indicator lights of a peer vehicle, an artificial neural network having a split neural network architecture, configured to recognize the activation state of the indicator lights of the peer vehicle, the artificial neural network comprising at least one spatial features extraction artificial neural network cell configured to extract spatial features of an output of the sensor of the indicator lights of the peer vehicle, a first temporal features extraction artificial neural network cell configured to extract temporal features of the output of the spatial features extraction artificial neural network cell, a second temporal features extraction artificial neural network cell configured to extract temporal features of the output of the spatial features extraction artificial neural network cell, wherein the first temporal features extraction artificial neural network cell is configured to determine changes in the activation state of the first kind of the indicator lights and to classify the first kind of indicator light as active or inactive, and the second temporal features extraction artificial neural network cell is configured to determine changes in the activation state of the second kind of the indicator lights and to classify the second kind of indicator light as active or inactive.