Knowledge Distillation for End-to-End Motion Planning in Autonomous Driving

Core Concepts

The author proposes PlanKD, a knowledge distillation method tailored for compressing end-to-end motion planners. By focusing on distilling planning-relevant features and incorporating a safety-aware waypoint-attentive mechanism, PlanKD offers a portable and safe solution for resource-limited deployment.

Abstract

The content introduces PlanKD, a novel knowledge distillation framework designed to compress end-to-end motion planners efficiently. By distilling planning-relevant information and prioritizing safety-critical waypoints, PlanKD significantly improves the performance of smaller planners while reducing reference time by approximately 50%. Extensive experiments demonstrate the effectiveness of PlanKD in enhancing the safety and portability of autonomous driving systems.

Stats

Inference Time (ms / frame): 78.3, 39.7, 22.8, 17.2, 10.7, 8.5, 7.2 Driving Score: 53.44, 36.55, 55.90, 17.12, 28.79, 11.96, 26.15 Collision Rate (#/km): 0.090, 0.121, 0.094, 0.362, 0.315,1 .117 ,0 .361

Quotes

"PlanKD can boost the performance of smaller planners by a large margin." "Our method can lower the reference time by approximately 50%." "Experiments illustrate that our PlanKD can improve the performance of smaller planners by a large margin."

Key Insights Distilled From

On the Road to Portability

by Kaituo Feng,... at arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.01238.pdf

Deeper Inquiries

How does PlanKD compare to traditional model compression techniques in terms of efficiency and performance

PlanKD offers a unique approach to model compression compared to traditional techniques. Traditional methods often focus on reducing the size of the neural network by pruning or quantization, which can lead to a drop in performance as the model loses important information. In contrast, PlanKD leverages knowledge distillation to transfer essential knowledge from a larger teacher model to a smaller student model. This targeted approach ensures that only relevant information is distilled, leading to improved efficiency and performance. By distilling planning-relevant features and using safety-aware waypoint-attentive distillation, PlanKD enhances the student model's ability to mimic crucial waypoints accurately while maintaining overall safety. This results in significant improvements in driving scores and route completion rates while reducing reference time by approximately 50%. In comparison, traditional compression techniques may struggle to balance efficiency with performance when applied to complex tasks like autonomous driving.

What potential challenges or limitations could arise when implementing PlanKD in real-world autonomous driving systems

Implementing PlanKD in real-world autonomous driving systems may present several challenges and limitations. One potential challenge is ensuring that the distilled knowledge effectively transfers from the teacher model to the student model without losing critical information. The complexity of driving scenarios and the need for accurate waypoint emulation require careful tuning of parameters and attention mechanisms within PlanKD. Another challenge could be related to computational resources and inference time. While PlanKD aims to compress models for deployment on resource-constrained systems, there may still be limitations in terms of processing power and memory capacity on edge devices used in autonomous vehicles. Balancing efficient inference with high-performance standards will be crucial for successful implementation. Moreover, real-world scenarios introduce unpredictable factors such as varying weather conditions, road layouts, and interactions with other vehicles or pedestrians. Ensuring that the compressed models trained using PlanKD can adapt effectively to these dynamic environments poses another challenge that needs consideration during implementation.

How might advancements in knowledge distillation impact other fields beyond autonomous driving

Advancements in knowledge distillation through methods like PlanKD have far-reaching implications beyond autonomous driving. In fields such as computer vision, natural language processing (NLP), robotics, healthcare diagnostics, finance modeling, etc., where large-scale deep learning models are prevalent but resource constraints exist for deployment purposes - knowledge distillation can play a vital role. For example: Computer Vision: Knowledge distillation can help compress large image recognition models for efficient deployment on edge devices. Natural Language Processing: Distilled language models can enable faster text generation or sentiment analysis applications. Robotics: Compressed neural networks through knowledge distillation can enhance robot perception capabilities while optimizing computational resources. Healthcare Diagnostics: Smaller yet accurate medical imaging models derived through distillation can aid doctors in diagnosing diseases more efficiently. Overall advancements in knowledge distillation not only improve efficiency but also democratize access to AI technologies by making them more accessible across various domains with limited resources available for computation-intensive tasks.