用FPGA实现可个性化驾驶风格识别的神经模糊传感器
An FPGA-Based Neuro-Fuzzy Sensor for Personalized Driving Assistance
- 基于FPGA的神经模糊系统，从真实驾驶数据中学习驾驶风格
- 实时处理速度达0.53微秒，满足高端主动安全系统要求
- 无需人为干预即可自适应调整跟车时距，适合智能辅助驾驶
高级驾驶辅助系统（ADAS）旨在自动化驾驶任务并提升行车与车辆安全。本文提出一种用于驾驶风格（DS）识别的智能神经模糊传感器，适用于ADAS增强。该传感器基于SHRP2研究中的自然驾驶数据，包含CAN总线、惯性测量单元和前向雷达信息。系统采用Xilinx Zynq可编程片上系统（PSoC）的FPGA器件实现，能够模拟一组驾驶员的典型时间参数，并调参以建模个体驾驶风格。该神经模糊传感器支持高速实时主动ADAS运行，可自动个性化行为至安全范围，无需驾驶员干预。特别地，针对稳态跟车场景下ACC系统的跟车时距（THW）参数个性化流程已开发完成，性能达到0.53微秒，满足前沿主动ADAS技术规格要求。
原文摘要 · Abstract (English)
Advanced driving-assistance systems (ADAS) are intended to automatize driver tasks, as well as improve driving and vehicle safety. This work proposes an intelligent neuro-fuzzy sensor for driving style (DS) recognition, suitable for ADAS enhancement. The development of the driving style intelligent sensor uses naturalistic driving data from the SHRP2 study, which includes data from a CAN bus, inertial measurement unit, and front radar. The system has been successfully implemented using a field-programmable gate array (FPGA) device of the Xilinx Zynq programmable system-on-chip (PSoC). It can mimic the typical timing parameters of a group of drivers as well as tune these typical parameters to model individual DSs. The neuro-fuzzy intelligent sensor provides high-speed real-time active ADAS implementation and is able to personalize its behavior into safe margins without driver intervention. In particular, the personalization procedure of the time headway (THW) parameter for an ACC in steady car following was developed, achieving a performance of 0.53 microseconds. This performance fulfilled the requirements of cutting-edge active ADAS specifications.
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