ParkDiffusion: Heterogeneous Multi-Agent Multi-Modal Trajectory Prediction for Automated Parking using Diffusion Models (2505.00586v1)

Abstract: 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.

Analysis of ParkDiffusion: A Novel Approach to Multi-Agent Trajectory Prediction for Automated Parking

The research paper titled "ParkDiffusion: Heterogeneous Multi-Agent Multi-Modal Trajectory Prediction for Automated Parking using Diffusion Models" introduces ParkDiffusion, a novel trajectory prediction framework specifically designed for automated parking scenarios. This paper addresses a critical aspect of Advanced Driver Assistance Systems (ADAS), focusing on accurate trajectory prediction necessary for seamless integration of perception and planning modules. The paper significantly extends the domain of trajectory prediction, traditionally concentrated on urban traffic or pedestrian-only scenarios, by incorporating heterogeneous agents in automated parking environments.

Methodology and Innovations

ParkDiffusion leverages diffusion models to manage the uncertainty and multi-modality inherent in trajectory prediction tasks. It includes several novel components:

• Dual Map Encoder: This encoder separately processes soft semantic cues (e.g., lane markings) and hard geometric constraints (e.g., parked vehicles), with a two-step cross-attention mechanism to fuse these features. Such distinction ensures that both navigational guidance and static obstacles are addressed effectively.
• Adaptive Agent Type Embedding: A specialized embedding module dynamically conditions trajectory predictions based on agent characteristics, differentiating between vehicles and pedestrians. This specificity accommodates the varied behaviors and interactions between distinct road users in parking scenarios.
• Kinematic Refinement: By outputting control signals leading to physically feasible trajectories, the model ensures kinematic viability, thus enhancing realism in its predictions. This involves a sophisticated approach to model the kinematics of different agent types, ensuring predictions adhere to realistic physical constraints.

These components collectively support the goal of predicting multi-modal trajectories, accounting for the complex and unpredictable dynamics of parking environments where vehicles and pedestrians intermingle.

Evaluation and Results

The efficacy of ParkDiffusion is assessed using the Dragon Lake Parking (DLP) and Intersections Drone (inD) datasets. The results exhibit ParkDiffusion’s superiority against various baseline models like MultiPath++, SceneTransformer, and SIMPL, especially in pedestrian trajectory prediction metrics. Notable improvements in metrics such as minimum Average Displacement Error (minADE), minimum Final Displacement Error (minFDE), and Miss Rate (MR) underscore its advanced performance across both datasets. These evaluations reveal ParkDiffusion’s capacity to significantly reduce prediction errors and improve safety outcomes in parking environments, highlighting its prowess in multi-agent trajectory forecasting.

Theoretical and Practical Implications

The research provides critical insights into the deployment of diffusion models tailored for parking scenarios, expanding their application beyond conventional urban traffic modeling. The ability to accurately predict trajectories for heterogeneous agents—vehicles and pedestrians—within less structured environments exemplifies ParkDiffusion's comprehensive approach to safety and efficiency in automated parking systems. Moreover, this paper opens avenues for integrating physics-based kinematic constraints with advanced generative models to produce realistic and feasible predictions, setting a new baseline in trajectory prediction frameworks.

Directions for Future Research

While ParkDiffusion demonstrates robust performance, the paper hints at potential extensions, such as incorporating additional agent types, which could further enrich understanding of diverse urban road environments and complex interactions. The framework’s adaptability to various traffic scenarios marks a promising direction for future advancements, particularly in optimizing real-time trajectory prediction applications by addressing the computational demands of diffusion models.

In summary, ParkDiffusion represents a significant step forward in heterogeneous multi-agent trajectory prediction, providing both theoretical advancements and practical applications for automated parking systems. Its innovative use of diffusion models and attention-based features to address uncertainties and agent-specific conditions sets a high benchmark for future research in ADAS technologies.