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Automated vehicles operating in urban environments have to reliably interactwith other traffic participants. Planning algorithms often utilize separateprediction modules forecasting probabilistic, multi-modal, and interactivebehaviors of objects. Designing prediction and planning as two separate modulesintroduces significant challenges, particularly due to the interdependence ofthese modules. This work proposes a deep learning methodology to combineprediction and planning. A conditional GAN with the U-Net architecture istrained to predict two high-resolution image sequences. The sequences representexplicit motion predictions, mainly used to train context understanding, andpixel state values suitable for planning encoding kinematic reachability,object dynamics, safety, and driving comfort. The model can be trained offlineon target images rendered by a sampling-based model-predictive planner,leveraging real-world driving data. Our results demonstrate intuitive behaviorin complex situations, such as lane changes amidst conflicting objectives.

Pixel State Value Network for Combined Prediction and Planning in Interactive Environments