Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations (2008.05930v1)

Abstract: In this paper we propose a novel end-to-end learnable network that performs joint perception, prediction and motion planning for self-driving vehicles and produces interpretable intermediate representations. Unlike existing neural motion planners, our motion planning costs are consistent with our perception and prediction estimates. This is achieved by a novel differentiable semantic occupancy representation that is explicitly used as cost by the motion planning process. Our network is learned end-to-end from human demonstrations. The experiments in a large-scale manual-driving dataset and closed-loop simulation show that the proposed model significantly outperforms state-of-the-art planners in imitating the human behaviors while producing much safer trajectories.

• The paper introduces a semantic occupancy representation that integrates perception, prediction, and planning to reduce collision rates by 40%.
• The methodology uses an end-to-end learnable framework validated on large-scale driving datasets, yielding smoother, human-like trajectories.
• The findings suggest that integrating semantic cues enhances safety and interpretability in autonomous vehicle motion planning.

Safe Motion Planning Through Interpretable Semantic Representations: An Overview

The paper "Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations" presents an innovative approach to motion planning for autonomous vehicles. The researchers introduce a novel end-to-end learnable framework that blends perception, prediction, and motion planning, yielding interpretable intermediate representations which enhance the safety and consistency of self-driving systems.

Core Contributions

The key contribution of this paper is the introduction of a semantic occupancy representation that drives the motion planning process, ensuring that planning costs align with perception and prediction outputs. This semantic layer offers probabilistic representations over time and space, capturing the locations of various classes of objects, including vehicles, bicyclists, and pedestrians, potentially occluded ones. This approach addresses a major limitation in earlier motion planning frameworks which often produce inconsistent estimates due to their decoupled multi-task structures.

Numerical Results and Insights

Experiments conducted using a large-scale manual-driving dataset and state-of-the-art closed-loop simulation demonstrate that the proposed model surpasses existing planners in emulating human behavior while substantially reducing collision rates. The cumulative collision rate is decreased by 40% at 5 seconds compared to the closest competitor, showcasing the effectiveness of the semantic occupancy model. Furthermore, the trajectories generated by this model exhibit lower jerk values, indicating smoother rides and greater comfort.

Theoretical and Practical Implications

Theoretically, the paper advances the field by providing a novel mechanism to incorporate semantic knowledge into the decision-making stack of autonomous vehicles, enhancing interpretability without sacrificing performance. Practically, this methodology could significantly enhance vehicular safety by reducing the likelihood of collisions caused by misinterpretation or missed detection of actors in the driving environment.

Future Speculations in AI and Autonomous Driving

While the model demonstrates impressive capabilities, future work can focus on further optimizing the interpretability and computational efficiency of these semantic representations. As autonomous driving technology matures, integrating more complex environmental factors and extending this model's applicability to various driving conditions and locales will be crucial. Moreover, exploring heterogeneous sensor inputs beyond LiDAR and high-definition maps may provide richer semantic features for even more robust motion planning.

In conclusion, this paper provides a vital step forward in achieving safer autonomous driving through the use of interpretable semantic representations. This approach highlights the importance of tightly integrating perception, prediction, and planning processes in AV systems, thus paving the way for future innovations in intelligent transportation.