Predicting the future states of surrounding traffic participants and planning a safe, smooth, and socially compliant trajectory accordingly is crucial for autonomous vehicles. There are two major issues with the current autonomous driving system: the prediction module is often separated from the planning module and the cost function for planning is hard to specify and tune. To tackle these issues, we propose a differentiable integrated prediction-planning framework (DIPP) that can also learn the cost function from data. Specifically, our framework uses a differentiable nonlinear optimizer as the motion planner, which takes as input the predicted trajectories of surrounding agents given by the neural network and optimizes the trajectory for the autonomous vehicle, enabling all operations to be differentiable, including the cost function weights. The proposed framework is trained on a large-scale real-world driving dataset to imitate human driving trajectories in the entire driving scene and validated in both open-loop and closed-loop manners. The open-loop testing results reveal that the proposed method outperforms the baseline methods across a variety of metrics and delivers planning-centric prediction results, allowing the planning module to output trajectories close to those of human drivers. In closed-loop testing, the proposed method outperforms various baseline methods, showing the ability to handle complex urban driving scenarios and robustness against the distributional shift. Importantly, we find that joint training of planning and prediction modules achieves better performance than planning with a separate trained prediction module in both open-loop and closed-loop tests. Moreover, the ablation study indicates that the learnable components in the framework are essential to ensure planning stability and performance.
翻译:预测交通参与者的未来状况,规划一个安全、顺畅和符合社会要求的轨道,这对自主车辆至关重要。当前自主驾驶系统有两个主要问题:预测模块往往与规划模块分离,规划的成本功能很难具体和调和。为了解决这些问题,我们提议了一个不同的综合预测规划框架(DIPP),这个框架也可以从数据中学习成本功能。具体地说,我们的框架使用一个不同的非线性优化工具作为运动规划器,它将神经网络提供的周围代理商的预测轨迹作为输入,优化自主驾驶系统的轨迹,使所有业务都能够不同,包括成本功能权重。为了解决这些问题,我们提议的框架将是一个大型真实世界驱动数据集,以模拟整个驾驶场的人类驱动轨迹,并以开放通道和闭路模式的方式验证成本功能。 开放式操作测试结果显示,拟议的方法比各种计量和交付规划中心预测结果的基线方法要好,使得所有操作都能够进行不同程度的运行率预测结果,允许大规模真实性驱动数据模型进行模拟,从而显示一个更精确的流程模型,从而显示一个更精确的流程测试方法。