[Paper Review] Conditional Generative Neural System for Probabilistic Trajectory Prediction

Jiachen Li, Hengbo Ma+1 more|arXiv (Cornell University)|May 5, 2019

Autonomous Vehicle Technology and Safety40 references18 citations

TL;DR

This paper proposes a Conditional Generative Neural System (CGNS) for probabilistic trajectory prediction that jointly models static context, dynamic interactions, and kinematic feasibility using conditional latent space learning and variational divergence minimization. By integrating soft attention mechanisms and differentiable barrier functions, CGNS generates diverse, realistic, and feasible trajectory hypotheses, outperforming baselines on pedestrian and roundabout driving datasets, especially in long-term prediction.

ABSTRACT

Effective understanding of the environment and accurate trajectory prediction of surrounding dynamic obstacles are critical for intelligent systems such as autonomous vehicles and wheeled mobile robotics navigating in complex scenarios to achieve safe and high-quality decision making, motion planning and control. Due to the uncertain nature of the future, it is desired to make inference from a probability perspective instead of deterministic prediction. In this paper, we propose a conditional generative neural system (CGNS) for probabilistic trajectory prediction to approximate the data distribution, with which realistic, feasible and diverse future trajectory hypotheses can be sampled. The system combines the strengths of conditional latent space learning and variational divergence minimization, and leverages both static context and interaction information with soft attention mechanisms. We also propose a regularization method for incorporating soft constraints into deep neural networks with differentiable barrier functions, which can regulate and push the generated samples into the feasible regions. The proposed system is evaluated on several public benchmark datasets for pedestrian trajectory prediction and a roundabout naturalistic driving dataset collected by ourselves. The experimental results demonstrate that our model achieves better performance than various baseline approaches in terms of prediction accuracy.

Motivation & Objective

• To develop a unified generative model that captures complex interactions and environmental context in multi-agent trajectory prediction.
• To improve long-term prediction accuracy by modeling both static scene context and dynamic agent interactions.
• To ensure generated trajectories are physically feasible and compliant with kinematic constraints through differentiable regularization.
• To enable diverse, realistic trajectory sampling by approximating the true data distribution using latent space and variational inference.
• To validate the model on both benchmark pedestrian datasets and a custom roundabout driving dataset for real-world robustness.

Proposed method

• The CGNS framework uses a deep feature extractor with soft attention to encode scene images and historical trajectories into multi-level representations.
• A conditional latent space is learned via an encoder that models dependencies between agents and environmental context.
• A generator network samples future trajectories from the learned latent distribution, enabling diverse hypothesis generation.
• The system integrates variational divergence minimization to approximate the true trajectory distribution using both explicit and implicit density learning.
• Differentiable barrier functions are introduced to embed soft kinematic constraints into the loss function, ensuring feasibility of generated trajectories.
• A block attention mechanism processes historical trajectories, while a Gaussian mixture attention mask focuses on salient regions in scene images.

Experimental results

Research questions

• RQ1 Can a unified generative model effectively combine static context, dynamic interactions, and kinematic constraints for multi-agent trajectory prediction?
• RQ2 How does attention-based feature selection improve prediction accuracy in complex, interactive environments?
• RQ3 To what extent does incorporating differentiable feasibility constraints enhance the realism and physical plausibility of generated trajectories?
• RQ4 How does the model's performance scale with prediction horizon compared to linear and recurrent baselines?
• RQ5 Does the integration of latent space learning and variational divergence minimization lead to better distribution approximation than standalone methods?

Key findings

• CGNS achieves the lowest average displacement error (ADE) and final displacement error (FDE) across multiple benchmarks, especially outperforming baselines in long-term prediction.
• The model with both context image attention and trajectory block attention (T + I + CLSL + VDM) shows significant performance gains over partial configurations as the prediction horizon increases.
• The ablation study confirms that context information becomes increasingly valuable with longer horizons, as road curvature significantly constrains vehicle motion.
• Qualitative results show that generated trajectories are smooth, realistic, and follow road curves, with ground truth located in the densest region of the predicted distribution.
• The attention mechanism successfully identifies relevant agents and scene regions, with higher attention weights on the predicted vehicle and its direction of travel, especially in high-interaction scenarios.
• The use of differentiable barrier functions effectively enforces kinematic feasibility, improving the realism of generated trajectories without compromising diversity.