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.

翻译：预测、决策与运动规划是自动驾驶的关键环节。在现有研究中，这些任务通常被设计为独立模块，或采用共享主干网络与独立任务头的多任务学习范式。然而，我们认为应将三者整合至统一框架。尽管近期部分研究遵循该思路，却存在输入表征复杂、框架设计冗余等问题，更关键的是无法对未来驾驶场景进行长期预测。针对上述问题，我们重新审视自动驾驶任务中各模块的必要性，并将必要模块融入极简自动驾驶框架。为此提出BEVGPT——一种集成驾驶场景预测、决策与运动规划的生成式预训练大模型。该模型以鸟瞰图（BEV）作为唯一输入源，基于周边交通场景做出驾驶决策。为保障轨迹可行性与平滑性，我们开发了基于优化的运动规划方法。在Lyft Level 5数据集上实现BEVGPT，并采用Woven Planet L5Kit进行真实驾驶仿真验证。实验表明，所提框架在100%的决策指标和66%的运动规划指标上超越既有方法，验证了其有效性与鲁棒性。此外，通过驾驶场景预测任务，证明了框架在长期生成BEV图像方面的卓越能力。据我们所知，这是首个仅以BEV图像为输入的、面向自动驾驶预测、决策与运动规划的生成式预训练大模型。