Medina Luis, Diez-Ochoa Miguel, Correal Raul, Cuenca-Asensi Sergio, Serrano Alejandro, Godoy Jorge, Martínez-Álvarez Antonio, Villagra Jorge
University Institute for Computing Research, University of Alicante, 03690 San Vicente del Raspeig, Spain.
Ixion Industry & Aerospace SL, Julian Camarilo 21B, 28037 Madrid, Spain.
Sensors (Basel). 2017 Nov 11;17(11):2599. doi: 10.3390/s17112599.
Grid-based perception techniques in the automotive sector based on fusing information from different sensors and their robust perceptions of the environment are proliferating in the industry. However, one of the main drawbacks of these techniques is the traditionally prohibitive, high computing performance that is required for embedded automotive systems. In this work, the capabilities of new computing architectures that embed these algorithms are assessed in a real car. The paper compares two ad hoc optimized designs of the Bayesian Occupancy Filter; one for General Purpose Graphics Processing Unit (GPGPU) and the other for Field-Programmable Gate Array (FPGA). The resulting implementations are compared in terms of development effort, accuracy and performance, using datasets from a realistic simulator and from a real automated vehicle.
基于融合来自不同传感器的信息及其对环境的稳健感知的汽车领域基于网格的感知技术正在该行业中迅速发展。然而，这些技术的主要缺点之一是传统上嵌入式汽车系统所需的计算性能过高且令人望而却步。在这项工作中，在一辆真实汽车中评估了嵌入这些算法的新型计算架构的能力。本文比较了贝叶斯占用滤波器的两种经过特别优化的设计；一种用于通用图形处理单元（GPGPU），另一种用于现场可编程门阵列（FPGA）。使用来自真实模拟器和真实自动驾驶车辆的数据集，从开发工作量、准确性和性能方面对最终实现进行了比较。
