BEVGPT：用于自动驾驶预测、决策与规划的生成式预训练大模型
BEVGPT: Generative Pre-trained Large Model for Autonomous Driving Prediction, Decision-Making, and Planning
AI总结：
本文提出BEVGPT，首个仅以鸟瞰图图像为输入的生成式预训练大模型，集成预测、决策与规划，在决策和运动规划指标上分别以100%和66%超越先前方法。
摘要：
预测、决策和运动规划对于自动驾驶至关重要。在当代大多数工作中，它们被视为独立的模块，或组合成一种多任务学习范式，该范式共享主干网络但具有独立的任务头。然而，我们认为它们应该被整合到一个综合框架中。尽管最近的一些方法遵循了这一方案，但它们存在输入表示复杂和框架设计冗余的问题。更重要的是，它们无法对未来驾驶场景进行长期预测。为了解决这些问题，我们重新思考了自动驾驶任务中每个模块的必要性，并仅将所需模块整合到一个极简的自动驾驶框架中。我们提出了BEVGPT，一个集成了驾驶场景预测、决策和运动规划的生成式预训练大模型。该模型仅以鸟瞰图（BEV）图像作为唯一输入源，并根据周围交通场景做出驾驶决策。为了确保驾驶轨迹的可行性和平滑性，我们开发了一种基于优化的运动规划方法。我们在Lyft Level 5数据集上实例化了BEVGPT，并使用Woven Planet L5Kit进行逼真的驾驶模拟。所提出框架的有效性和鲁棒性通过其在100%的决策指标和66%的运动规划指标上优于先前方法这一事实得到验证。此外，通过驾驶场景预测任务，证明了我们的框架能够长期准确生成BEV图像。据我们所知，这是首个仅以BEV图像作为输入的用于自动驾驶预测、决策和规划的生成式预训练大模型。
英文摘要：
Prediction, decision-making, and motion planning are essential for autonomous driving. In most contemporary works, they are considered as individual modules or combined into a multi-task learning paradigm with a shared backbone but separate task heads. However, we argue that they should be integrated into a comprehensive framework. Although several recent approaches follow this scheme, they suffer from complicated input representations and redundant framework designs. More importantly, they can not make long-term predictions about future driving scenarios. To address these issues, we rethink the necessity of each module in an autonomous driving task and incorporate only the required modules into a minimalist autonomous driving framework. We propose BEVGPT, a generative pre-trained large model that integrates driving scenario prediction, decision-making, and motion planning. The model takes the bird's-eye-view (BEV) images as the only input source and makes driving decisions based on surrounding traffic scenarios. To ensure driving trajectory feasibility and smoothness, we develop an optimization-based motion planning method. We instantiate BEVGPT on Lyft Level 5 Dataset and use Woven Planet L5Kit for realistic driving simulation. The effectiveness and robustness of the proposed framework are verified by the fact that it outperforms previous methods in 100% decision-making metrics and 66% motion planning metrics. Furthermore, the ability of our framework to accurately generate BEV images over the long term is demonstrated through the task of driving scenario prediction. To the best of our knowledge, this is the first generative pre-trained large model for autonomous driving prediction, decision-making, and motion planning with only BEV images as input.
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