Scene-Centric Trajectory Prediction Networks via Knowledge Distillation
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Solution Overview
Problem
Existing agent-centric trajectory prediction neural networks for autonomous vehicles face computational bottlenecks and latency issues, making them unsuitable for on-board deployment, while scene-centric networks are more efficient but often lack performance.
Innovation Solution
Training a scene-centric trajectory prediction neural network using an already trained agent-centric network through knowledge distillation, allowing for reduced computational requirements and latency without sacrificing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If agent-centric trajectory prediction neural networks are used, then prediction accuracy is improved, but computational cost and latency increase quadratically with the number of agents
Solution Approach 1:
The patent segments the trajectory prediction problem into two distinct approaches: agent-centric networks that process each agent independently for high accuracy, and scene-centric networks that process the entire scene globally for computational efficiency. The system selectively applies the appropriate segmentation strategy based on the specific prediction task and resource constraints, resolving the contradiction between accuracy and efficiency.
Solution Approach 2:
The patent inverts the traditional agent-centric approach by introducing scene-centric networks that process the scene as a whole rather than individual agents. This inversion allows the system to achieve linear scaling with the number of agents while maintaining competitive prediction accuracy, effectively resolving the quadratic computational bottleneck of traditional methods.
2Measurement precision
If agent-centric neural networks are deployed on-board autonomous vehicles, then prediction accuracy is maintained, but latency requirements cannot be met due to quadratic scaling
Solution Approach 1:
The patent introduces dynamic adaptability by enabling the system to switch between agent-centric and scene-centric network architectures based on real-time requirements. When low latency is critical, the system dynamically selects scene-centric networks; when maximum accuracy is needed and computational resources permit, it uses agent-centric networks. This dynamic approach resolves the static contradiction between accuracy and latency.
Solution Approach 2:
The patent changes the fundamental parameter of network architecture from agent-centric to scene-centric, which alters the computational complexity from quadratic to linear scaling. This parameter change enables the system to meet strict latency requirements on autonomous vehicles while maintaining acceptable prediction accuracy through the scene-centric processing approach.
3Productivity
If scene-centric trajectory prediction neural networks are used, then computational efficiency and latency are improved, but prediction accuracy decreases compared to agent-centric networks
Solution Approach 1:
The patent creates a universal trajectory prediction system that incorporates both agent-centric and scene-centric network architectures. This multi-functional system can select the appropriate network type based on the specific application requirements, whether prioritizing accuracy or efficiency. The hybrid approach allows scene-centric networks to handle cases where computational efficiency is paramount while agent-centric networks handle cases requiring maximum accuracy, resolving the contradiction through universal applicability.
4Reliability
If agent-centric networks process all agents in a scene, then comprehensive prediction coverage is achieved, but computational cost scales quadratically with scene complexity
Solution Approach 1:
The patent segments the computational workload by dividing scene processing into agent-centric and scene-centric components. The scene-centric network handles global scene understanding and interactions efficiently, while agent-centric networks focus on individual agent predictions where detailed accuracy is critical. This segmentation reduces overall computational complexity from quadratic to near-linear while maintaining comprehensive prediction coverage.
Solution Approach 2:
The patent applies partial action by using scene-centric networks for agents where full agent-centric processing is not necessary, and reserving agent-centric processing for critical agents requiring high accuracy. This selective application of computational resources achieves comprehensive coverage while avoiding the excessive quadratic computational cost of applying agent-centric processing uniformly to all agents in the scene.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training trajectory prediction neural networks using distillation.