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2024 | OriginalPaper | Buchkapitel

PnP: Integrated Prediction and Planning for Interactive Lane Change in Dense Traffic

verfasst von : Xueyi Liu, Qichao Zhang, Yinfeng Gao, Zhongpu Xia

Erschienen in: Neural Information Processing

Verlag: Springer Nature Singapore

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Abstract

Making human-like decisions for autonomous driving in interactive scenarios is crucial and difficult, requiring the self-driving vehicle to reason about the reactions of interactive vehicles to its behavior. To handle this challenge, we provide an integrated prediction and planning (PnP) decision-making approach. A reactive trajectory prediction model is developed to predict the future states of other actors in order to account for the interactive nature of the behaviors. Then, n-step temporal-difference search is used to make a tactical decision and plan the tracking trajectory for the self-driving vehicle by combining the value estimation network with the reactive prediction model. The proposed PnP method is evaluated using the CARLA simulator, and the results demonstrate that PnP obtains superior performance compared to popular model-free and model-based reinforcement learning baselines.

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Titel: PnP: Integrated Prediction and Planning for Interactive Lane Change in Dense Traffic
verfasst von: Xueyi Liu
Qichao Zhang
Yinfeng Gao
Zhongpu Xia
Verlag: Springer Nature Singapore
Buch: Neural Information Processing
Print ISBN: 978-981-9980-75-8

Electronic ISBN: 978-981-9980-76-5

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-981-99-8076-5_22

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