Future trajectories of neighboring traffic agents have a significant influence on the path planning and decision-making of autonomous vehicles. While trajectory forecasting is a well-studied field, research mainly focuses on snapshot-based prediction, where each scenario is treated independently of its global temporal context. However, real-world autonomous driving systems need to operate in a continuous setting, requiring real-time processing of data streams with low latency and consistent predictions over successive timesteps. We leverage this continuous setting to propose a lightweight yet highly accurate streaming-based trajectory forecasting approach. We integrate valuable information from previous predictions with a novel endpoint-aware modeling scheme. Our temporal context propagation uses the trajectory endpoints of the previous forecasts as anchors to extract targeted scenario context encodings. Our approach efficiently guides its scene encoder to extract highly relevant context information without needing refinement iterations or segment-wise decoding. Our experiments highlight that our approach effectively relays information across consecutive timesteps. Unlike methods using multi-stage refinement processing, our approach significantly reduces inference latency, making it well-suited for real-world deployment. We achieve state-of-the-art streaming trajectory prediction results on the Argoverse 2 multi-agent and single-agent benchmarks, while requiring substantially fewer resources.

Trajectory prediction is an essential component of the perception stack in autonomous mobile robots (AMRs). AMRs operate in complex environments where their movements are influenced by various environment elements, such as racks and storage locations. Therefore, accurate and efficient trajectory prediction for intralogistics requires detailed environment modeling that goes beyond the lane-based context mainly used in road traffic methods. We propose the addition of a new environment context encoder module that can be seamlessly integrated into state-of-the-art autonomous driving systems. Our approach, tailored to the specific challenges of intralogistics, achieves highly accurate predictions using compact and efficient baseline networks.

Trajectory prediction for autonomous driving has been extensively studied using large-scale datasets from the US and Asia. These datasets typically have a strong bias toward predicting vehicle motion. Recently, the View-of-Delft Prediction (VoD-P) dataset introduced a collection of European urban mixed-traffic scenarios, posing unique challenges due to its diversity and relatively small dataset size. In this work, we conduct a detailed study on trajectory prediction on the VoD-P dataset. We show that state-of-the-art trajectory prediction models, which perform well on large-scale vehicle-biased datasets, struggle to generalize to the scenarios. To address this limitation, we propose a simple yet effective transformer-based trajectory prediction model, specifically designed to handle the challenges posed in diverse urban scenarios. Combining a strong baseline with refined environment modeling, our approach significantly outperforms all existing methods on the VoD-P dataset.

Understanding actions of other agents increases the efficiency of autonomous mobile robots (AMRs) since they encompass intention and indicate future movements. We propose a new method that allows us to infer vehicle actions using a shallow image-based classification model. The actions are classified via bird’s-eye view scene crops, where we project the detections of a 3D object detection model onto a context map. We learn map context information and aggregate temporal sequence information without requiring object tracking. This results in a highly efficient classification model that can easily be deployed on embedded AMR hardware. To evaluate our approach, we create new large-scale synthetic datasets showing warehouse traffic based on real vehicle models and geometry.

For efficient and safe autonomous driving, it is essential that autonomous vehicles can predict the motion of other traffic agents. While highly accurate, current motion prediction models often impose significant challenges in terms of training resource requirements and deployment on embedded hardware. We propose a new efficient motion prediction model, which achieves highly competitive benchmark results while training only a few hours on a single GPU. Due to our lightweight architectural choices and the focus on reducing the required training resources, our model can easily be applied to custom datasets. Furthermore, its low inference latency makes it particularly suitable for deployment in autonomous applications with limited computing resources.