ParkDiffusion: Heterogeneous Multi-Agent Multi-Modal Trajectory Prediction for Automated Parking using Diffusion Models

Automated parking is a critical feature of Advanced Driver Assistance Systems (ADAS), where accurate trajectory prediction is essential to bridge perception and planning modules. Despite its significance, research in this domain remains relatively limited, with most existing studies concentrating on single-modal trajectory prediction of vehicles. In this work, we propose ParkDiffusion, a novel approach that predicts the trajectories of both vehicles and pedestrians in automated parking scenarios. ParkDiffusion employs diffusion models to capture the inherent uncertainty and multi-modality of future trajectories, incorporating several key innovations. First, we propose a dual map encoder that processes soft semantic cues and hard geometric constraints using a two-step cross-attention mechanism. Second, we introduce an adaptive agent type embedding module, which dynamically conditions the prediction process on the distinct characteristics of vehicles and pedestrians. Third, to ensure kinematic feasibility, our model outputs control signals that are subsequently used within a kinematic framework to generate physically feasible trajectories. We evaluate ParkDiffusion on the Dragon Lake Parking (DLP) dataset and the Intersections Drone (inD) dataset. Our work establishes a new baseline for heterogeneous trajectory prediction in parking scenarios, outperforming existing methods by a considerable margin.

BaSAL: Size Balanced Warm Start Active Learning for LiDAR Semantic Segmentation

Active learning strives to reduce the need for costly data annotation, by repeatedly querying an annotator to label the most informative samples from a pool of unlabeled data and retraining a model from these samples. We identify two problems with existing active learning methods for LiDAR semantic segmentation. First, they ignore the severe class imbalance inherent in LiDAR semantic segmentation datasets. Second, to bootstrap the active learning loop, they train their initial model from randomly selected data samples, which leads to low performance and is referred to as the cold start problem. To address these problems we propose BaSAL, a size-balanced warm start active learning model, based on the observation that each object class has a characteristic size. By sampling object clusters according to their size, we can thus create a size-balanced dataset that is also more class-balanced. Furthermore, in contrast to existing information measures like entropy or CoreSet, size-based sampling does not require an already trained model and thus can be used to address the cold start problem. Results show that we are able to improve the performance of the initial model by a large margin. Combining size-balanced sampling and warm start with established information measures, our approach achieves a comparable performance to training on the entire SemanticKITTI dataset, despite using only 5% of the annotations, which outperforms existing active learning methods. We also match the existing state-of-the-art in active learning on nuScenes. Our code will be made available upon paper acceptance.