Exploiting Real-Time FPGA Based Adaptive Systems Technology for Real-Time Sensor Fusion in Next Generation Automotive Safety Systems

TL;DR

This paper introduces a real-time FPGA-based sensor fusion system for automotive safety, demonstrating accurate sensor alignment correction using inertial measurement units and Kalman filtering, surpassing industry standards.

Contribution

It presents a platform-neutral, FPGA-based sensor fusion approach with a custom soft processor, applying aviation-grade Kalman filtering techniques to automotive sensor alignment.

Findings

Sensor misalignment can be accurately computed using inexpensive accelerometers and IMUs.

Test results exceeded industry requirements by an order of magnitude.

Real-time correction improves automotive sensor reliability.

Abstract

We present a system for the boresighting of sensors using inertial measurement devices as the basis for developing a range of dynamic real-time sensor fusion applications. The proof of concept utilizes a COTS FPGA platform for sensor fusion and real-time correction of a misaligned video sensor. We exploit a custom-designed 32-bit soft processor core and C-based design & synthesis for rapid, platform-neutral development. Kalman filter and sensor fusion techniques established in advanced aviation systems are applied to automotive vehicles with results exceeding typical industry requirements for sensor alignment. Results of the static and the dynamic tests demonstrate that using inexpensive accelerometers mounted on (or during assembly of) a sensor and an Inertial Measurement Unit (IMU) fixed to a vehicle can be used to compute the misalignment of the sensor to the IMU and thus vehicle. In some cases the model predications and test results exceeded the requirements by an order of magnitude with a 3-sigma or 99% confidence.