Abstract
With the rise of high resolution multiple input multiple output (MIMO) systems, radar became an important sensor in the development of Advanced Driver Assistance Systems (ADAS) and autonomous driving applications. Autonomous driving will rely strongly on artificial intelligence. Since most modern classification algorithms are based on neural networks, they require huge amounts of data to perform well, especially in unexpected traffic situations. Radar sensor simulation can potentially produce a great variety of training data for machine learning algorithms, which makes it an important cornerstone in the development of ADAS. Furthermore, with radar simulators, different antenna configurations and various edge cases can be simulated. In this work, a versatile ray tracing toolchain based on the shoot and bouncing rays (SBR) approach is presented. The program is able to simulate complex urban environments including realistic clutter, by utilizing simplistic reflection models. The program does not only produce realistic radar images, but also generates camera-like images using the same materials. Furthermore, this work deals with the adaption of the SBR method to radar sensors with an arbitrary number of transmit (TX)- and receive (RX) antennas, which enables the simulation of large MIMO arrays. A novel performance optimization approach is proposed for large numbers of TX antennas, which reduces the runtime dramatically. The quality of the simulation is verified by measuring a complex and realistic scenario with a high resolution automotive MIMO radar. Also, a study of the effect on quality and runtime is being investigated for various optimization approaches, including the proposed method.
Highlights
Radar sensors offer various advantages compared to Lidar or camera sensors, such as being comparably cheap and working in the absent of light and under harsh weather conditions
In this work we proposed a radar ray tracing simulator based on a novel simplistic material model
Problems occurring for the simulation with multiple antennas have been solved and the results show that the simulator can reproduce measurement data realistically
Summary
Radar sensors offer various advantages compared to Lidar or camera sensors, such as being comparably cheap and working in the absent of light and under harsh weather conditions. The authors in [17] claim that ray tracing methods offer realistic simulations in respect of multipath and occlusion The drawback of these approaches is that they are computationally expensive, sophisticated to implement and require a detailed model of the environment. The main goal of our simulator is to generate data sets for radar sensors with an arbitrary antenna and modulation scheme For these reasons the SBR method fits our needs best, especially as simulations in real-time are not required for the training of neutral networks and development purposes in general. The contributions of this work are the following: 1) The implementation of a simplistic material model adapted from visual ray tracing approaches, which are able to produce realistic radar data, and camera-like images, which makes prototyping of radar and camera fusion algorithms possible.
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