BEVWorld: A Multimodal World Simulator for Autonomous Driving via Scene-Level BEV Latents (2407.05679v3)

Abstract: World models have attracted increasing attention in autonomous driving for their ability to forecast potential future scenarios. In this paper, we propose BEVWorld, a novel framework that transforms multimodal sensor inputs into a unified and compact Bird's Eye View (BEV) latent space for holistic environment modeling. The proposed world model consists of two main components: a multi-modal tokenizer and a latent BEV sequence diffusion model. The multi-modal tokenizer first encodes heterogeneous sensory data, and its decoder reconstructs the latent BEV tokens into LiDAR and surround-view image observations via ray-casting rendering in a self-supervised manner. This enables joint modeling and bidirectional encoding-decoding of panoramic imagery and point cloud data within a shared spatial representation. On top of this, the latent BEV sequence diffusion model performs temporally consistent forecasting of future scenes, conditioned on high-level action tokens, enabling scene-level reasoning over time. Extensive experiments demonstrate the effectiveness of BEVWorld on autonomous driving benchmarks, showcasing its capability in realistic future scene generation and its benefits for downstream tasks such as perception and motion prediction.

• The paper introduces BEVWorld, a multimodal model that unifies visual and LiDAR data into a BEV latent space for precise future scenario predictions.
• It employs a self-supervised encoder-decoder framework and latent sequence diffusion to mitigate error accumulation during long sequence forecasting.
• Empirical results on nuScenes and CARLA demonstrate improved 3D detection metrics and reduced motion prediction errors, highlighting its practical impact on autonomous systems.

An Overview of BEVWorld: A Multimodal World Model for Autonomous Driving

The paper presents BEVWorld, a multimodal world model designed to enhance autonomous driving systems by integrating various sensor data into a unified Bird's Eye View (BEV) latent space. This innovative approach addresses several challenges faced by contemporary autonomous systems, such as the heavy reliance on extensive labeled datasets and the difficulty of integrating heterogeneous sensor data. BEVWorld effectively utilizes self-supervised learning frameworks to construct an environment model capable of predicting future scenarios, thus facilitating improved decision-making processes.

Core Methodology

BEVWorld is structured around two primary components: a multimodal tokenizer and a latent BEV sequence diffusion model. The multimodal tokenizer is tasked with compressing and transforming input modalities—namely visual and LiDAR data—into a unified BEV latent space. This is accomplished through an encoder-decoder framework that operates in a self-supervised manner. The reconstruction of high-resolution image and point cloud outputs is achieved through a novel rendering technique employing ray-based methodologies.

The tokenized BEV space then serves as input for the latent sequence diffusion network, which employs a spatial-temporal transformer to predict future multi-view scenarios. This methodology overcomes the limitations of autoregressive models by mitigating error accumulation over long sequences, facilitating the simultaneous prediction of future frames.

Empirical Evaluation

The experiments conducted on benchmark datasets, such as nuScenes and CARLA, clearly demonstrate the efficacy of BEVWorld in generating realistic future scenarios and effectively supporting downstream tasks like 3D detection and motion prediction. BEVWorld notably improves detection metrics in a 3D object detection task, achieving significant relative performance gains when the pre-trained tokenizer is employed, with improvements in both NDS and mAP metrics.

On the motion prediction front, BEVWorld decreases the error metrics (minADE and minFDE) significantly, showcasing its utility in anticipating future states accurately. These advances are achieved without the dependency on manual labeling, which is particularly noteworthy given the increasing reliance on autonomous systems operating in diverse and unstructured environments.

Implications and Future Directions

The introduction of a multimodal world model like BEVWorld signifies a crucial step forward in enhancing the autonomy of driving systems. Not only does it facilitate the interpretation and prediction of complex real-world scenarios, but it also provides a framework for leveraging vast amounts of unlabeled data efficiently.

The implications of BEVWorld extend into practical realms, where real-time scenario prediction and decision-making are critical. By generating comprehensive BEV representations, this model lays the groundwork for the development of more intelligent and reliable autonomous vehicles.

In theoretical terms, this approach could be explored further to refine the integration of diverse data streams and the scalability of world models. Enhancements might focus on refining the precision of dynamic object rendering and optimizing the computational efficiency of diffusion processes.

Moreover, BEVWorld serves as a potential foundational model for future AI systems aimed at tackling complex spatial-temporal reasoning tasks. By continuing to build on this conceptual framework, autonomous technologies can benefit from increased accuracy, adaptability, and robustness in varied operational contexts. As the field progresses, leveraging the full potential of world models will undoubtedly unlock new avenues for innovation and advancement in autonomous systems.

In sum, BEVWorld offers a substantial contribution to the understanding and application of multimodal reinforcement learning in autonomous systems, combining theoretical advances with practical application potential in autonomous driving technologies.