Hamilton-Jacobi reachability analysis is a powerful technique used to verify the safety of autonomous systems. This method is very good at handling non-linear system dynamics with disturbances and flexible set representations. A drawback to this approach is that it suffers from the curse of dimensionality, which prevents real-time deployment on safety-critical systems. In this paper, we show that a customized hardware design on a Field Programmable Gate Array (FPGA) could accelerate 4D grid-based Hamilton-Jacobi (HJ) reachability analysis up to 16 times compared to an optimized implementation and 142 times compared to MATLAB ToolboxLS on a 16-thread CPU. Our design can overcome the complex data access pattern while taking advantage of the parallel nature of the HJ PDE computation. Because of this, we are able to achieve real-time formal verification with a 4D car model by re-solving the HJ PDE at a frequency of 5Hz on the FPGA as the environment changes. The latency of our computation is deterministic, which is crucial for safetycritical systems. Our approach presented here can be applied to different systems dynamics, and moreover, potentially leveraged for higher dimensions systems. We also demonstrate obstacle avoidance with a robot car in a changing environment.
翻译:汉密尔顿- 贾科比( Hamilton- Jacobi) 的可达性分析是用于核查自主系统安全的有力技术,这种方法非常有助于处理非线性系统动态,具有扰动性和灵活设置的表达方式。 这种方法的一个缺点是,它受到维度诅咒的影响,这妨碍了安全临界系统的实时部署。 在本文中,我们表明,外地可编程门阵列(FPGA)的定制硬件设计可以加速4D网格式的可达性分析,比优化实施速度要快16次,比MATLAB工具框LS在16thread CPU上的可达142次。 我们的设计可以克服复杂的数据存取模式,同时利用HJ PDE的平行性计算方法。 因此,我们能够通过在环境变化中以5Hz频率重新解开HJ PDE来实现4D汽车模型的实时正式核查。 我们的计算方式是确定性,对于安全临界系统至关重要。 我们在此提出的方法可以克服复杂的数据模式,同时利用不同的系统, 也有可能将一个障碍运用。