From Antenna Design to High Fidelity, Full Physics Automotive Radar Sensor Corner Case Simulation

Abstract

Advanced driver assistance systems (ADAS) have recently been thrust into the spotlight in the automotive industry as carmakers and technology companies pursue effective active safety systems and fully autonomous vehicles. Various sensors such as lidar (light detection and ranging), radar (radio detection and ranging), ultrasonic, and optical cameras are employed to provide situational awareness to vehicles in a highly dynamic environment. Radar has emerged as a primary sensor technology for both active/passive safety and comfort-advanced driver-assistance systems. Physically building and testing radar systems to demonstrate reliability is an expensive and time-consuming process. Simulation emerges as the most practical solution to designing and testing radar systems. This paper provides a complete, full physics simulation workflow for automotive radar using finite element method and asymptotic ray tracing electromagnetic solvers. The design and optimization of both transmitter and receiver antennas is presented. Antenna interaction with vehicle bumper and fascia is also investigated. A full physics-based radar scene corner case is modelled to obtain high-fidelity range-Doppler maps. Finally, this paper investigates the effects of inclined roads on late pedestrian detection and the effects of construction metal plate radar returns on false target identification. Possible solutions are suggested and validated. Results from this study show how pedestrian radar returns can be increased by over 16 dB for early detection along with a 27 dB reduction in road construction plate radar returns to suppress false target identification. Both solutions to the above corner cases can potentially save pedestrian lives and prevent future accidents.

Methodical Approach to the Development of a Radar Sensor Model for the Detection of Urban Traffic Participants Using a Virtual Reality Engine

“…However, as with the antenna model, the problem arises that the resulting field needs to be discrete. Equation (8) shows an approach similar to the one used in the antenna model to numerically integrate the power density. This assumes that a ray represents the power integrated by the density up to the next ray.…”

“…However, simple algorithms are used, that do not sufficiently simulate the microwaves of the radar. Compared to this, more complex approaches like [6] [7] [8] [9] and [10] are using realistic sending, reflection and receiving models to accurately simulate the radar. But these models do not aim to do the simulation in real-time.…”

High Fidelity Physics Simulation of 128 Channel MIMO Sensor for 77GHz Automotive Radar

“…Finally, HFSS SBR+ also corrects the PO current truncation at shadow boundaries by including creeping wave (CW) physics. Therefore, using GO, PO, UTD, PTD and CW, high-fidelity physics based synthetic radar returns can be obtained [25], [26]. Using 8 T x elements with a spacing of 8λ and 16 R x elements with spacing of λ/2, a 128 virtual channel sensor was designed in SBR+.…”

“…It has been estimated that 8.8 billion test-driving miles will be needed before autonomous vehicles are deemed safe for [24]. Simulation has emerged as a practical, relatively inexpensive and safe approach for ADAS sensor validation [25]- [30].…”

Novel Mra-Based Sparse Mimo and Simo Antenna Arrays for Automotive Radar Applications

“…Front-facing radars make use of LRRs and MRRs to detect obstacles that appear in front of the automobile. The radar determines the range and the relative velocities of the obstacles (relative to its velocity) and categorizes them into different range-doppler bins [3]. This radar technique cannot distinguish two objects that fall in the same range-doppler bin.…”