Pixel State Value Network for Combined Prediction and Planning in Interactive Environments
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.
Code (0)
등록된 구현이 없습니다.
Tasks
PredictionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Trajectory Planning for Non-Communicating Mobile Robots using Inverse Optimal Control
To enable an efficient interaction of non-communicating mobile robots in collision avoidance scenarios, we present a novel combined trajectory planning and prediction algorithm. Inverse optimal control is used to estimat…
Collision AvoidanceTrajectory PlanningPAT: Pixel-wise Adaptive Training for Long-tailed Segmentation
Beyond class frequency, we recognize the impact of class-wise relationships among various class-specific predictions and the imbalance in label masks on long-tailed segmentation learning. To address these challenges, we …
Hierarchical Width-Based Planning and Learning
Width-based search methods have demonstrated state-of-the-art performance in a wide range of testbeds, from classical planning problems to image-based simulators such as Atari games. These methods scale independently of …
Atari GamesCounterfactual Regularization for Model-Based Reinforcement Learning
In sequential tasks, planning-based agents have a number of advantages over model-free agents, including sample efficiency and interpretability. Recurrent action-conditional latent dynamics models trained from pixel-leve…
counterfactualmodelModel-based Reinforcement Learningreinforcement-learning+2Post Prognostic Decision for Predictive Maintenance Planning with Remaining Useful Life Uncertainty
This paper investigates the use of the Particle Swarm Optimization (PSO) algorithm to quantify the effect of RUL uncertainty on predictive maintenance planning. The prediction of RUL is influenced by many sources of unce…