Conditional Generative Neural System for Probabilistic Trajectory Prediction (1905.01631v2)

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.

• The paper introduces a unified framework that combines latent space learning with attention mechanisms to generate probabilistic trajectory distributions.
• It employs block attention and Gaussian mixture attention masks to enhance prediction accuracy and computational efficiency.
• Experimental validations on ETH, UCY, SDD, and custom roundabout datasets demonstrate its superior performance over baseline methods.

Conditional Generative Neural System for Probabilistic Trajectory Prediction

The paper introduces a Conditional Generative Neural System (CGNS) for probabilistic trajectory prediction, which is targeted primarily towards autonomous vehicles and mobile robotics operating in complex environments. This approach effectively addresses the inherent uncertainty of future dynamics, which is crucial for safety and optimal decision-making in autonomous navigation systems.

CGNS leverages a combination of conditional latent space learning and variational divergence minimization, augmented by attention mechanisms focusing on both static context and interaction data. The CGNS framework is designed to learn and predict distributions of future trajectories, enabling the generation of realistic, diverse, and feasible trajectory pathways. There are notable contributions within the paper:

1. Framework Design: The paper introduces a unified predictive framework integrating both trajectory and scene data, powered by a generative neural system. Such a combination enables accurate approximations of trajectory distributions for multiple interactive agents.
2. Attention Mechanisms: Development of a block attention mechanism for trajectory analysis and a Gaussian mixture attention mask for scrutinizing scene image sequences are highlighted as innovations for computational efficiency and superior prediction accuracy.
3. Soft Constraint Regularization: Implementation of a regularization method using differentiable barrier functions further enhances the system's ability to conform generated samples within physically feasible regions, particularly important for applications involving vehicle dynamics.
4. Experimental Validation: Robust validation across several datasets, including ETH, UCY, SDD, and a dataset created by the authors at a roundabout scenario, evidences that CGNS outperforms existing baseline methods in trajectory prediction tasks.

Implications and Future Directions

The introduction of the CGNS framework suggests strong implications for both practical applications and theoretical advancements in the field of trajectory prediction for intelligent systems. Practically, its capability to produce diverse and realistic trajectory predictions enhances safety metrics and decision-making quality in autonomous vehicle systems, impacting the design of Advanced Driver Assistance Systems (ADAS) and fully autonomous navigation modules.

Theoretically, combining latent space learning with adversarial training into a single framework opens up several avenues for further research, particularly in enhancing prediction models for dynamic, multi-agent systems. Moreover, attention mechanisms can be extended and fine-tuned to improve computational efficiency, making the system more scalable and adaptable to different environments.

Future developments could explore the expansion of the generative system to handle additional forms of context data, such as sensor fusion data from LiDAR, radar, or infrared sensors. Furthermore, integrating this system with real-time data augmentation and adaptive learning models could prove beneficial in fast-evolving scenarios, broadening its applicability in domains demanding real-time predictive capabilities. As AI continually evolves, such robust and comprehensive frameworks are indispensable in pushing forward the capabilities of autonomous navigation technologies.