The design of Autonomous Vehicle software has largely followed the Sense-Plan-Act model. Traditional modular AV stacks develop perception, planning, and control software separately with little integration when optimizing for different objectives. On the other hand, end-to-end methods usually lack the principle provided by model-based white-box planning and control strategies. We propose a computationally efficient method for approximating closed-form trajectory generation with interpolating Radial Basis Function Networks to create a middle ground between the two approaches. The approach creates smooth approximations of local Lipschitz continuous maps of feasible solutions to parametric optimization problems. We show that this differentiable approximation is efficient to compute and allows for tighter integration with perception and control algorithms when used as the planning strategy.

翻译：自主车辆软件的设计在很大程度上遵循了感知-规划-控制模型。传统模块化自动驾驶系统分别开发感知、规划与控制软件，在优化不同目标时缺乏集成性。另一方面，端到端方法通常缺乏基于模型的白盒规划与控制策略所提供的原理性指导。我们提出一种计算高效的方法，通过插值径向基函数网络逼近封闭形式的轨迹生成，从而在这两种方法之间建立折中方案。该方法能够构造参数优化问题可行解局部Lipschitz连续映射的光滑逼近。实验证明，这种可微近似计算高效，当作为规划策略使用时，可实现与感知、控制算法更紧密的集成。