Kinematic Parameters Calibration for Automotive Millimeter-Wave Radars

In this tutorial paper, we systematically present the fundamental operating principles and analytical details of the discrete Fourier transform based signal processing techniques for the TDM MIMO FMCW millimeter-wave (mmWave) automotive radars. The mmWave radars provide a key sensing capability to support safety features of the conventional and autonomous vehicles. Automotive radar sensors are used to detect presence and location of the objects of interest to derive comprehensive and accurate knowledge of road conditions and surrounding environments. Automotive radars are subjected to an increasing demand to provide high-resolution measurements in the range-Doppler-azimuth-elevation domains. Therefore, the current state-of-the-art automotive radars commonly employ MIMO technologies, resulting in a large block of multidimensional data to process in real time through a long chain of signal processing algorithms. Detailed coverage on the fundamental radar signal processing techniques are scattered in a large body of literature for classical radar systems. Currently, there is no tutorial available that covers the technical details of the latest TDM MIMO FMCW radar technology, making it extremely hard for new researchers in the field of automotive mmWave radars. This paper contains sufficient technical details to serve as a tutorial. It can help lay a solid analytical foundation to facilitate exploration and development of advanced radar signal processing techniques for automotive applications. The algorithmic details and analytical results presented in this paper can be readily applied to both real-time implementation and post-processing. Simulation and experimental results are presented in this paper to validate analytical derivations and to demonstrate the capabilities of the TDM MIMO radar sensor in practical implementations.

Abstract. Automotive radar and lidar sensors represent key components for next generation driver assistance functions (Jones, 2001). Today, their use is limited to comfort applications in premium segment vehicles although an evolution process towards more safety-oriented functions is taking place. Radar sensors available on the market today suffer from low angular resolution and poor target detection in medium ranges (30 to 60m) over azimuth angles larger than ±30°. In contrast, Lidar sensors show large sensitivity towards environmental influences (e.g. snow, fog, dirt). Both sensor technologies today have a rather high cost level, forbidding their wide-spread usage on mass markets. A common approach to overcome individual sensor drawbacks is the employment of data fusion techniques (Bar-Shalom, 2001). Raw data fusion requires a common, standardized data interface to easily integrate a variety of asynchronous sensor data into a fusion network. Moreover, next generation sensors should be able to dynamically adopt to new situations and should have the ability to work in cooperative sensor environments. As vehicular function development today is being shifted more and more towards virtual prototyping, mathematical sensor models should be available. These models should take into account the sensor's functional principle as well as all typical measurement errors generated by the sensor.

Radar sensors are one of the key elements in highly automated driving systems. Their performance is robust independently of weather and visibility conditions, being able to detect targets up to hundreds of meters distance. In vehicles, radar sensors are required to estimate the range, doppler shift and direction of arrival of the reflected wave. In this contribution we will focus on the direction of arrival (DoA) estimation, which, while being crucial for environmental perception, still has considerable room for improvement. In particular, we aim to measure the performance of Compressive Sensing (CS) based algorithms applied for reconstruction of the DoA. In contrast to the traditional FFT approach, these algorithms are able to exploit sparse antenna configurations with a reduced number of Tx and Rx channels and a large effective aperture. For this purpose, a series of relevant metrics are defined and applied to measurements in representative open-air driving scenarios acquired with an automotive 4x8 MIMO radar operating at 77GHz. The results show that, when compared with the FFT, these algorithms display an overall enhanced angular estimation accuracy, resolution and false alarm ratio.

Identifying objects with radar is in great demand to avoid road accidents. Recent research tried to identify moving objects on a road by inputting radar information to a machine learning classifier. In the conventional method, the features used in the machine learning are extracted from the observed radar information with a short time interval. Since the movement of the objects is different depending on the objects, time series information is effective for classification, which has not been exploited before. In this paper, we propose an on-road object identification considering time series of radar information. We measured objects with 79.5 GHz millimeter-wave radar and extract features from a series of time windows by calculating the mean and variance of object information, i.e., velocity, distance, and signal power. The classification performance was evaluated with a dataset obtained by on-road experiments. It is shown that our method outperforms the conventional one and the proposed features significantly contribute to the accurate identification.

Over the past ten years millimetre-wave automotive radar has matured from laboratory prototype to mass production for vehicle applications. Manufacturers are now producing the second or third generation of sensors as designs mature, volumes increase and costs are reduced. New application with greater potential for even larger production volume and lower cost are now being developed. Automotive radar systems are being developed and deployed in the 24 GHz band using narrowband modulation. Development work is also underway to produce ultra-wideband radar sensors using the 76-81 GHz band. Current applications for radar in vehicles includes adaptive cruise control, and stop & go and blind sport warning. Future applications include lane change warning, rear closing vehicle warning, collision warning, pedestrian detection, cross traffic warning, collision mitigation, side impact pre warning and air bag pre warming

Modern millimeter-wave automotive radars are employed to keep a safe distance between vehicles and reduce the collision risk when driving. Meanwhile, an on-board radar module is supposed to operate in the line-of-sight condition, which limits its sensing capabilities in intersections with obstructed visibility. Therefore, this paper investigates the scheme with passive reflector, enabling the automotive radar to detect an approaching vehicle in the non-line-of-sight (blind) urban intersection. First, extensive radar measurements of the backscattering power are carried out with the in-house assembled millimeter-wave radar equipment. Next, the measured data is employed to calibrate an accurate analytical model, deduced and described in this paper. Finally, the analytical models are deployed to define the optimal parameters of the radar scheme in the particular geometry of the selected intersection scenario. Specifically, it is found that the optimal angular orientation of the reflector is 43.5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> , while the 20 m curvature radius shows better performance compared to a flat reflector. Specifically, the curved convex shape increases scattering power by 20 dB in the shadow region and, thus, improves the detection probability of the vehicle, approaching the blind intersection.

We present a deep reinforcement learning approach to design an automotive radar system with integrated sensing and communication. In the proposed system, sparse transmit arrays with quantized phase shifter are used to carry out transmit beamforming to enhance the performance of both radar sensing and communication. Through interaction with environment, the automotive radar learns a reward that reflects the difference between mainlobe peak and the peak sidelobe level in radar sensing mode or communication user feedback in communication mode, and intelligently adjust its beamforming vector. The Wolpertinger policy based action-critic network is introduced for beamforming vector learning, which solves the dimension curse due to huge beamforming action space.

Laser radar provides better spatial resolution than millimeter-wave radar (MWR) due to the high directivity of the laser beam. However, commercial in-car laser radar approximates the target speed by a range differentiation method, which has the problems of excessive time consumption and the introduction of large errors. In this paper, a new Doppler laser radar scheme for simultaneously measuring target range and speed in automotive applications is presented. The scheme includes a new laser radar architecture, a new method to modulate the transmitted signal, and a method for calculating the range and speed from the signal returned from the target. The length of the transmitted signal is several microseconds, giving the possibility of realizing a high scan speed in automotive applications. In addition, simulations based on Simulink/Matlab were carried out to validate the proposed scheme. Finally, an experimental demonstration of simultaneous range and speed measurements was performed. The moving target for the experiment was a highly reflecting sheet attached to an electric grinder.

There is a growing interest of car manufacturers in sensors monitoring the car's surrounding area in order to improve safety systems from mere crash survival to crash prediction or prevention by early detection of hazardous situations. Therefore radar sensors have been intensively investigated for many years. A large variety of radar based vehicular sensors have been developed. Narrow-beam radars are tested for intelligent cruise control (ICC), collision or obstacle warning, other sensors are used for stop&go operation, as lane change aid, for side or rear impact warning. Though first radar sensors for ICC operation are moving into the pre-production phase, further improvement in low-cost high-volume manufacturing is essential Next generations of automotive radar sensors will take advantage of increased signal processing power, progress in semiconductor technology, and improved packaging and assembly techniques. Future radar sensors will comprise electronically scanned beams allowing for better resolution with a high image update rate. Fusion of radar with other on board sensors (e.g. video) will further improve the performance of car safety systems, leading to intelligent vehicles in future.

The 24 GHz continuous-wave (CW) Doppler radar sensor (DRS) is widely used for measuring the instantaneous speed of moving objects by using a non-contact approach, and has begun to be used in train-borne movable speed measurements in recent years in China because of its advanced performance. The architecture and working principle of train-borne DRSs with different structures including single-channel DRSs used for freight train speed measurements in railway freight dedicated lines and dual-channel DRSs used for speed measurements of high-speed and urban rail trains in railway passenger dedicated lines, are first introduced. Then, the disadvantages of two traditional speed calibration methods for train-borne DRS are described, and a new speed calibration method based on the Doppler shift signal simulation by imposing a signal modulation on the incident CW microwave signal is proposed. A 24 GHz CW radar target simulation system for a train-borne DRS was specifically realized to verify the proposed speed calibration method for a train-borne DRS, and traceability and performance evaluation on simulated speed were taken into account. The simulated speed range of the simulation system was up to (5~500) km/h when the simulated incident angle range was within the range of (45 ± 8)°, and the maximum permissible error (MPE) of the simulated speed was ±0.05 km/h. Finally, the calibration and uncertainty evaluation results of two typical train-borne dual-channel DRS samples validated the effectiveness and feasibility of the proposed speed calibration approach for a train-borne DRS with full range in the laboratory as well as in the field.

We employ a 77–81 GHz frequency-modulated continuous-wave (FMCW) millimeter-wave radar to sense anomalous vibrations during vehicle transport at highway speeds for the first time. Secure metallic containers can be breached during transport by means of drilling into their sidewalls but detecting a drilling signature is difficult because the large vibrations of transport drown out the small vibrations of drilling. For the first time, we demonstrate that it is possible to use a non-contact millimeter-wave radar sensor to detect this micron-scale intrusive drilling while highway-speed vehicle movement shakes the container. With the millimeter-wave radar monitoring the microdoppler signature of the container’s vibrating walls, we create a novel signal-processing pipeline consisting of range–angle tracking, time–frequency analysis, horizontal stripe image convolution, and principal component analysis to create a robust and powerful detection statistic to alarm if drilling is present. To support this pipeline, we develop a statistical model combining the vibrating container and the random vibrations induced by vehicle movement to explore the robustness of the sensor’s detection capabilities. The presented results strongly support the inclusion of a millimeter-wave radar vibration sensor into a transport security system.

Autonomous driving is expected to be realized in the future with the development of information and communication technology (ICT). However, the reliability of autonomous vehicles (AVs) in complex environments needs major improvement. To facilitate the autonomous driving, high-precision and low-cost traffic flow detection is essential for driving decision and traffic surveillance, and has drawn increasing attention from both academia and industry. In this paper, we propose a novel cost-effective IoT-based traffic flow detection scheme with particular focuses on vehicle counting and speed measurement. To this end, microwave Doppler radar sensors with a unit price of $2.3 are utilized to collect the traffic data of passing vehicles on the road. Then, a multi-threshold detection algorithm is proposed to extract features for vehicle counting and speed measurement. After this, experiments are carried out in different scenarios to evaluate the proposed traffic flow detection scheme. The results validate the high-precision detection with average 98.3% vehicle counting accuracy and 95.8% speed measurement accuracy.

The issue of interference between automotive radars is an emerging topic. Although different interference suppression techniques and mitigation approaches have been previously stud-ied, none of them have been evaluated considering complex traffic scenarios where multiple vehicles are equipped with several radar sensors. This paper presents two different interference avoidance strategies: Random Frequency Hopping with Sub-Channels and the Compass Method. The effectiveness of the strategies is evaluated by simulating two complex scenarios: A highway with 105 vehicles and an intersection of two streets with 34 vehicles, each vehicle containing five radar sensors. Subsequently, the strategies are validated by taking measurements considering the intersection of two streets.

The emerging 4D-imaging automotive MIMO radar sensors necessitate the selection of appropriate transmit wave-forms, which should be separable on the receive side in addition to having low auto-correlation sidelobes. TDM, FDM, DDM, and inter-chirp CDM approaches have traditionally been proposed for FMCW radar sensors to ensure the orthogonality of the transmit signals. However, as the number of transmit antennas increases, each of the aforementioned approaches suffers from some drawbacks, which are described in this paper. PMCW radars, on the other hand, can be considered to be more costly to implement, have been proposed to provide better performance and allow for the use of waveform optimization techniques. In this context, we use a block gradient descent approach to design a waveform set for MIMO-PMCW that is optimized based on weighted integrated sidelobe level in this paper, and we show that the proposed waveform outperforms conventional MIMO-FMCW approaches by performing comparative simulations.

Automotive radar sensors play a vital role in the development of autonomous driving due to their ability to detect objects even under adverse conditions [1] . Besides that, radar sensors offer long range capability and compact size, which enables the implementation in tight spaces. The environmental sensing task in autonomous vehicles must be validated due the severity of an operational failure that endangers human life. Carrying out these validation tests in the field however, presents a risk to other traffic participants and involves a great effort since distances in the order of several hundred million kilometer have to be covered to ensure the proper functioning of the system [2] . Also, these test are not repeatable. For this reason, a so-called Vehicle-in-the-Loop (ViL) approach is considered using a end-of-line vehicle placed on a steerable chassis dynamometer with the ability to simulate real world test scenarios. Such Radar Target Simulators (RTS) are capable of performing end-of-line, over-the-air validation tests by looping back a received signal and afterwards modified radar signal. It is essential, that these simulated targets are very close to reality and can also be compared to test drive data. However, this requires that all involved phenomena are investigated and implemented correctly. The radar signal is emitted from the sensor, travels through the radar radio channel, is reflected at an object, and travels back through the radio channel to the receiver, which in the case of radar is typically at the same location as the transmitter. Wave propagation must therefore take into account multiple reflections and scattering from objects in addition to free space propagation. To take the scattering or the backscattering into account, the so-called RCS pattern is usually used. However, since modern automotive radars are capable of resolving individual scattering centers well in angle and distance, considering the target as a point scatterer is far from sufficient. Therefore, a measurement system is presented that is able to measure road users with high resolution. This is done by using a mmW FMCW Radar Sensor working in the frequency range from 76-81 GHz which is equipped with a highly-directive lens antenna. Several traffic participants like cars, bikes, motor bikes and vulnerable road users are measured in detail. Also, measurements are compared to different full wave simulations. However, the results of these measurements cannot be implemented into a Radar Target Simulators, since the number of antennas that are necessary to generate the scattering points is limited, e.g. four antennas [3] . For this reason, the measured scattering points must be combined into relevant scattering clusters, which can then also be simulated with the limited number of antennas of the radar target simulator. The used radar target simulators in this approach is capable of generating echoes by using eight antennas. Each of the scattering clusters is consequently implanted in the wave propagation by its specific RCS pattern.