Sascha Rosbach

Sascha Rosbach, Stefan M. Leupold, Simon Großjohann et al.

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

Sascha Rosbach, Xing Li, Simon Großjohann et al.

General-purpose trajectory planning algorithms for automated driving utilize complex reward functions to perform a combined optimization of strategic, behavioral, and kinematic features. The specification and tuning of a single reward function is a tedious task and does not generalize over a large set of traffic situations. Deep learning approaches based on path integral inverse reinforcement learning have been successfully applied to predict local situation-dependent reward functions using features of a set of sampled driving policies. Sample-based trajectory planning algorithms are able to approximate a spatio-temporal subspace of feasible driving policies that can be used to encode the context of a situation. However, the interaction with dynamic objects requires an extended planning horizon, which depends on sequential context modeling. In this work, we are concerned with the sequential reward prediction over an extended time horizon. We present a neural network architecture that uses a policy attention mechanism to generate a low-dimensional context vector by concentrating on trajectories with a human-like driving style. Apart from this, we propose a temporal attention mechanism to identify context switches and allow for stable adaptation of rewards. We evaluate our results on complex simulated driving situations, including other moving vehicles. Our evaluation shows that our policy attention mechanism learns to focus on collision-free policies in the configuration space. Furthermore, the temporal attention mechanism learns persistent interaction with other vehicles over an extended planning horizon.