Forecasting the motion of others in shared spaces is a key for intelligent agents to operate safely and smoothly. We present an approach for probabilistic prediction of pedestrian motion incorporating various context cues. Our approach is based on goal-oriented prediction, yielding interpretable results for the predicted pedestrian intention, even without the prior knowledge of goal positions. By using Markov chains, the resulting probability distribution is deterministic—a beneficial property for motion planning or risk assessment in automated and assisted driving. Our approach outperforms a physics-based approach and improves over state-of-the-art approaches by reducing standard deviations of prediction errors and improving robustness against realistic, noisy measurements.

Reliable multi-agent trajectory prediction is crucial for the safe planning and control of autonomous systems. Compared with single-agent cases, the major challenge in simultaneously processing multiple agents lies in modeling complex social interactions caused by various driving intentions and road conditions. Previous methods typically leverage graph-based message propagation or attention mechanism to encapsulate such interactions in the format of marginal probabilistic distributions. However, it is inherently sub-optimal. In this paper, we propose IPCC-TP, a novel relevance-aware module based on Incremental Pearson Correlation Coefficient to improve multi-agent interaction modeling. IPCC-TP learns pairwise joint Gaussian Distributions through the tightly-coupled estimation of the means and covariances according to interactive incremental movements. Our module can be conveniently embedded into existing multi-agent prediction methods to extend original motion distribution decoders. Extensive experiments on nuScenes and Argoverse 2 datasets demonstrate that IPCC-TP improves the performance of baselines by a large margin.

Predicting trajectories of participating vehicles is a crucial task towards full and safe autonomous driving. General unconstrained machine learning methods often report unrealistic predictions, and need to be combined with different motion constraints. Existing work either defines some shallow maneuvers and modes to regulate the output, or uses vehicle dynamics as the main source of constraints, for instance via kinematic models. In contrast, we present a new approach that guides the learning models by complex semantic maneuvers, constructing from both vehicle states and the surrounding objects. We propose a novel Maneuver Fusion layer to incorporate the logic-based semantic maneuvers into deep neural networks. We also incorporate and refine the different loss functions to account for the feasibility of the trajectories, adapting to different maneuver types. Finally, we design a hierarchical multi-task learning framework with adaptive loss to provide a multimodal trajectory prediction. Our method was evaluated on a large-scale real world data set for urban driving and was shown to give promising improvement over the states of the art.