An FPGA Overlay for Efficient Real-Time Localization in 1/10th Scale Autonomous Vehicles

Abstract

Heterogeneous systems-on-chip (HeSoC) based on reconfigurable accelerators, such as Field-Programmable Gate Arrays (FPGA), represent an appealing option to deliver the performance/Watt required by the advanced perception and localization tasks employed in the design of Autonomous Vehicles. Different from software-programmed GPUs, FPGA development involves significant hardware design effort, which in the context of HeSoCs is further complicated by the system-level integration of HW and SW blocks. High-Level Synthesis is increasingly being adopted to ease hardware IP design, allowing engineers to quickly prototype their solutions. However, automated tools still lack the required maturity to efficiently build the complex hardware/software interaction between the host CPU and the FPGA accelerator(s). In this paper we present a fully integrated system design where a particle filter for LiDAR-based localization is efficiently deployed as FPGA logic, while the rest of the compute pipeline executes on programmable cores. This design constitutes the heart of a fully-functional 1/10th-scale racing autonomous car. In our design, accelerated IPs are controlled locally to the FPGA via a proxy core. Communication between the two and with the host CPU happens via shared memory banks also implemented as FPGA IPs. This allows for a scalable and easy-to-deploy solution both from the hardware and software viewpoint, while providing better performance and energy efficiency compared to state-ofthe-art solutions.

A Survey of Automotive Radar and Lidar Signal Processing and Architectures

“…Another computationally heavy task is the weather denoising of the point-cloud; this can also be accelerated in hardware, as proposed in [101]. Real-time lidar localization can also be accelerated; in particular, Bernardi et al [102] present a hardware accelerator for particle filter on an FPGA platform to perform this task in real time.…”