Generative End-to-End Autonomous Driving: Modeling Future Trajectory Distributions for Improved Motion Prediction and Planning

核心概念

The proposed GenAD framework models autonomous driving as a generative problem, learning a structured latent space to capture the prior of realistic trajectories. This enables simultaneous motion prediction and planning by sampling from the learned distributions conditioned on the instance-centric scene representation.

摘要

The paper proposes a Generative End-to-End Autonomous Driving (GenAD) framework that models autonomous driving as a future trajectory generation problem. The key components are:

Instance-centric scene representation:

Extracts multi-scale image features and transforms them into a bird's-eye view (BEV) representation.
Refines BEV tokens into map and agent tokens using cross-attention.
Incorporates high-order interactions between the ego vehicle and other agents using self-attention.
Injects map information into the instance-centric representation using cross-attention.

Trajectory prior modeling:

Learns a variational autoencoder to map ground-truth trajectories to a structured latent space, modeling the uncertainty and prior of realistic trajectories.

Latent future trajectory generation:

Employs a gated recurrent unit (GRU) to model the temporal evolution of instances in the learned latent space.
Uses a simple MLP-based decoder to generate waypoints from the latent representations.

The proposed GenAD framework can simultaneously perform motion prediction and planning by sampling from the learned distributions conditioned on the instance-centric scene representation. Extensive experiments on the nuScenes dataset show that GenAD achieves state-of-the-art performance on vision-centric end-to-end autonomous driving with high efficiency.

統計資料

"Vision-centric autonomous driving has been extensively explored in recent years due to its economic convenience."
"Most existing end-to-end autonomous driving models are composed of several modules and follow a pipeline of perception, motion prediction, and planning."
"We argue that the serial design of prediction and planning of existing pipelines ignores the possible future interactions between the ego car and the other traffic participants."
"Future trajectories are highly structured and share a common prior (e.g., most trajectories are continuous and straight lines). Still, most existing methods fail to consider this structural prior, leading to inaccurate predictions and planning."

引述

"We argue that the conventional progressive pipeline still cannot comprehensively model the entire traffic evolution process, e.g., the future interaction between the ego car and other traffic participants and the structural trajectory prior."
"We model autonomous driving as a future generation problem and conduct motion prediction and ego planning simultaneously in a structural latent trajectory space."

從以下內容提煉的關鍵洞見

GenAD

by Wenzhao Zhen... 於 arxiv.org 04-09-2024

https://arxiv.org/pdf/2402.11502.pdf

深入探究

How can the proposed generative framework be extended to handle more complex driving scenarios, such as intersections or merging lanes, where the interactions between agents become more intricate

To handle more complex driving scenarios like intersections or merging lanes, where the interactions between agents are more intricate, the proposed generative framework can be extended in several ways:

Multi-Agent Interaction Modeling: Incorporating more sophisticated interaction modeling techniques, such as graph neural networks or attention mechanisms, to capture the complex relationships between multiple agents in dynamic scenarios like intersections.

Hierarchical Planning: Implementing a hierarchical planning approach where the system can plan at different levels of abstraction, from high-level route planning to low-level trajectory generation, to navigate through complex scenarios effectively.

Dynamic Environment Modeling: Integrating dynamic environment modeling to predict the behavior of other agents in real-time, considering factors like intent, uncertainty, and potential actions in response to the ego vehicle's movements.

Adaptive Trajectory Generation: Developing adaptive trajectory generation algorithms that can adjust trajectories in real-time based on the evolving environment and the actions of other agents, ensuring safe and efficient navigation through complex scenarios.

By incorporating these enhancements, the generative framework can better handle the intricacies of complex driving scenarios and improve the overall performance and safety of autonomous driving systems.

What other types of structural priors or latent representations could be explored to further improve the realism and diversity of the generated trajectories

To further improve the realism and diversity of the generated trajectories, the proposed framework can explore the following structural priors or latent representations:

Temporal Consistency: Incorporating temporal consistency constraints to ensure that the generated trajectories follow realistic motion patterns over time, considering factors like acceleration, deceleration, and smooth transitions between actions.

Semantic Context: Integrating semantic context information into the latent representations to capture higher-level scene understanding, such as road layouts, traffic rules, and environmental conditions, which can guide more context-aware trajectory generation.

Uncertainty Modeling: Enhancing the modeling of uncertainty in the latent space to account for unpredictable events or variations in agent behavior, enabling the system to generate diverse trajectories that cover a range of possible outcomes.

Behavior Prediction: Leveraging predictive modeling techniques to anticipate the future actions of other agents based on historical data, enabling the system to proactively plan trajectories that account for potential interactions and avoid collisions.

By exploring these additional structural priors and latent representations, the generative framework can generate more realistic, diverse, and contextually-aware trajectories for autonomous driving in complex environments.

Given the focus on vision-based autonomous driving, how could the proposed approach be adapted or combined with other sensor modalities, such as LiDAR or radar, to leverage their complementary strengths

Incorporating LiDAR or radar sensor modalities into the vision-based autonomous driving approach can enhance the system's perception capabilities and overall robustness. Here's how the proposed approach could be adapted or combined with other sensor modalities:

Sensor Fusion: Implementing sensor fusion techniques to combine information from vision, LiDAR, and radar sensors, leveraging the strengths of each modality to create a more comprehensive and accurate representation of the environment.

Multi-Modal Perception: Developing multi-modal perception models that can effectively integrate data from different sensors to improve object detection, localization, and scene understanding, enhancing the system's ability to perceive and react to its surroundings.

Complementary Information: Utilizing LiDAR for precise distance measurements and detailed 3D mapping, radar for detecting objects in adverse weather conditions or low visibility, and vision for high-resolution imaging and semantic understanding, to create a robust perception system that can adapt to various environmental challenges.

Redundancy and Safety: Leveraging multiple sensor modalities for redundancy and safety, ensuring that the system can maintain accurate perception and decision-making capabilities even in the presence of sensor failures or challenging scenarios.

By integrating vision-based approaches with LiDAR and radar sensor modalities, the autonomous driving system can benefit from a more comprehensive and reliable perception system, leading to improved performance, safety, and adaptability in diverse driving conditions.