OmniDrive: A Comprehensive LLM-Agent Framework for 3D Perception, Reasoning, and Planning in Autonomous Driving

Kernekoncepter

OmniDrive proposes a novel 3D vision-language model architecture and a comprehensive benchmark to enable strong 3D reasoning and planning capabilities for autonomous driving agents powered by large language models.

Resumé

The paper presents OmniDrive, a holistic framework for end-to-end autonomous driving with large language model (LLM) agents. The key contributions are:

OmniDrive-Agent: A novel 3D vision-language model architecture that uses sparse queries to lift and compress visual representations into 3D before feeding them into an LLM. This query-based representation allows joint encoding of dynamic objects and static map elements, providing a condensed world model for perception-action alignment in 3D.

OmniDrive-nuScenes: A new benchmark with comprehensive visual question-answering (VQA) tasks, including scene description, traffic regulation, 3D grounding, counterfactual reasoning, decision making and planning. This benchmark goes beyond single expert trajectories and challenges the models' true spatial understanding and planning capabilities in 3D.

The paper shows that OmniDrive-Agent demonstrates excellent reasoning and planning capabilities in complex 3D scenes, outperforming previous state-of-the-art methods on both perception-related and planning-related tasks.

Statistik

The proposed OmniDrive-Agent model can process high-resolution multi-view video input efficiently.
OmniDrive-Agent achieves comparable performance to state-of-the-art methods on open-loop 3D planning tasks, with collision rate of 0.0% and intersection rate of 0.56% in the 1-second horizon.
On the OmniDrive-nuScenes benchmark, OmniDrive-Agent achieves an average precision of 52.3% and average recall of 59.6% on counterfactual reasoning tasks.

Citater

"OmniDrive aims to provide a holistic framework for end-to-end autonomous driving with LLM-agents."
"Our framework starts with a novel 3D MLLM architecture that uses sparse queries to lift and compress visual representations into 3D before feeding them into an LLM."
"We further propose a new benchmark with comprehensive visual question-answering (VQA) tasks, including scene description, traffic regulation, 3D grounding, counterfactual reasoning, decision making and planning."

Vigtigste indsigter udtrukket fra

OmniDrive: A Holistic LLM-Agent Framework for Autonomous Driving with 3D Perception, Reasoning and Planning

by Shihao Wang,... kl. arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.01533.pdf

Dybere Forespørgsler

How can the proposed OmniDrive framework be extended to handle more complex interactions with other agents in a closed-loop setting?

The OmniDrive framework can be extended to handle more complex interactions with other agents in a closed-loop setting by incorporating real-time feedback and responses from these agents. In a closed-loop setting, the autonomous driving system needs to interact with and respond to the dynamic behavior of other vehicles, pedestrians, and objects in the environment. This can be achieved by integrating real-time sensor data from the surrounding agents, such as Lidar, radar, and camera inputs, into the framework.
One approach to enhance the framework for closed-loop interactions is to implement a predictive modeling component that can anticipate the future actions of other agents based on their current trajectories and behaviors. By incorporating predictive modeling algorithms, the system can proactively plan and adjust its own actions to ensure safe and efficient interactions with other agents on the road.
Furthermore, reinforcement learning techniques can be employed to train the autonomous driving system to adapt and learn from interactions with other agents in real-time. By using reinforcement learning algorithms, the system can continuously improve its decision-making processes based on feedback from the environment and other agents.
Overall, extending the OmniDrive framework for closed-loop interactions with other agents involves integrating real-time sensor data, predictive modeling, and reinforcement learning techniques to enable the autonomous driving system to navigate complex scenarios and interact safely with its surroundings.

What are the potential limitations of using counterfactual reasoning based on simulated trajectories, and how can the framework be improved to better capture the dynamic nature of real-world driving scenarios?

Using counterfactual reasoning based on simulated trajectories has some limitations when applied to real-world driving scenarios. One limitation is the potential discrepancy between simulated trajectories and actual real-world outcomes. Simulated trajectories may not always accurately reflect the complex and unpredictable nature of real-world driving scenarios, leading to suboptimal decision-making by the autonomous driving system.
To improve the framework and better capture the dynamic nature of real-world driving scenarios, several enhancements can be implemented:

Integration of Real-Time Data: Incorporating real-time sensor data from the environment can provide up-to-date information on the surrounding traffic, road conditions, and unexpected events. This real-time data can help the system make more informed decisions based on the current state of the environment.

Dynamic Environment Modeling: Enhancing the framework to dynamically model and adapt to changes in the environment, such as sudden obstacles, changing traffic patterns, and unpredictable behaviors of other agents. This adaptive modeling can improve the system's responsiveness to real-world dynamics.

Human Behavior Modeling: Incorporating models of human driver behavior and intentions can help the system anticipate and respond to the actions of human drivers on the road, enhancing safety and efficiency in interactions.

Validation and Testing: Rigorous validation and testing procedures using real-world driving data can help validate the effectiveness of the framework in capturing the dynamic nature of driving scenarios. Continuous testing and validation in diverse environments can improve the system's robustness and reliability.

By addressing these limitations and implementing these improvements, the framework can better capture the dynamic nature of real-world driving scenarios and enhance the autonomous driving system's decision-making capabilities in complex and unpredictable environments.

Given the advancements in multimodal learning, how can the OmniDrive framework leverage additional modalities beyond vision and language, such as audio or tactile information, to further enhance the autonomous driving capabilities?

The OmniDrive framework can leverage additional modalities beyond vision and language, such as audio or tactile information, to further enhance autonomous driving capabilities in the following ways:

Audio-Based Perception: Integrating audio sensors can provide valuable information about the surrounding environment, including sirens, honking, and other auditory cues. By analyzing audio data, the system can better understand the auditory environment and react to potential hazards or alerts.

Tactile Feedback: Incorporating tactile sensors or haptic feedback systems can enhance the system's awareness of physical interactions with the environment, such as detecting vibrations or pressure changes. This tactile feedback can be used to improve the system's response to road conditions, obstacles, and interactions with other agents.

Multimodal Fusion: Implementing multimodal fusion techniques to combine information from vision, language, audio, and tactile modalities can provide a more comprehensive understanding of the environment. By fusing data from multiple modalities, the system can make more informed decisions and adapt to diverse driving scenarios.

Emotion Recognition: Utilizing audio and visual cues for emotion recognition can help the system understand the emotional states of passengers or other road users. This information can be used to adjust the driving behavior to ensure a comfortable and safe experience for all occupants.

Environmental Sensing: Leveraging additional modalities like infrared sensors or environmental sensors can enhance the system's perception of the environment, especially in challenging conditions such as low visibility or adverse weather.

By integrating these additional modalities and leveraging multimodal learning techniques, the OmniDrive framework can enhance its perception capabilities, improve decision-making processes, and ultimately enhance the overall autonomous driving capabilities in diverse and complex driving scenarios.