Automotive Radar Interference Avoidance Strategies for Complex Traffic Scenarios

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.

This paper presents a novel approach for mutual interference mitigation in automotive radars based on advanced waveform design by imposing time and frequency diversity to frequency modulated continuous wave (FMCW) chirp signal. Performance of the proposed waveform has been investigated using software-defined radio transceiver, and measurement results are provided. The system concept is validated by developed automotive radar channel model considering realistic 3D road traffic scenario using a ray-tracing tool. Theoretical analysis and simulation examples of mutual interference scenarios in automotive environment are provided to evaluate the effectiveness of the proposed method. Performance of both conventional FMCW waveform and proposed waveform under mutual interference environments has been provided for fair comparison. Results show that the proposed system is able to detect the targets of interest successfully while mitigates the false targets in mutual interference environments.

The number of small sophisticated wireless sensors which share the electromagnetic spectrum is expected to grow rapidly over the next decade and interference between these sensors is anticipated to become a major challenge. In this paper we study the interference mechanisms in one such sensor, automotive radars, where our results are directly applicable to a range of other sensor situations. In particular, we study the impact of radar waveform design and the associated receiver processing on the statistics of radar–radar interference and its effects on sensing performance. We propose a novel interference mitigation approach based on pseudo-random cyclic orthogonal sequences (PRCOS), which enable sensors to rapidly learn the interference environment and avoid using frequency overlapping waveforms, which in turn results in a significant interference mitigation with analytically tractable statistical characterization. The performance of our new approach is benchmarked against the popular random stepped frequency waveform sequences (RSFWS), where both simulation and analytic results show considerable interference reduction. Furthermore, we perform experimental measurements on commercially available automotive radars to verify the proposed model and framework.

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.

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 kinematics parameters measurement of the target by the environmental perception radar sensor is the basic guarantee for the safe and efficient driving of intelligent vehicle. 24GHz millimeter-wave radar is distinct from 77GHz millimeter-wave radar in the speed, range and angle measurement performance due to the different working frequency band and bandwidth among the existed mainstream commercial automotive radar sensors. In order to evaluate the speed and range measurement performance of 24GHz and 77GHz millimeter-wave radar sensors, this paper develops and builds a set of kinematic parameters calibration facility for automotive millimeter-wave radars based on virtual instrument technology, and verifies the feasibility and simulation accuracy of the simulated calibration facility through the speed and range simulated calibration results of the 24GHz and 77GHz millimeter-wave radar samples. The results of simulated calibration and uncertainty evaluation comparison show that the speed measurement repeatability and accuracy of the 77GHz millimeter-wave radar sample are superior to that of the 24GHz millimeter-wave radar sample.

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.

The critical issue of mutual interference in automotive radar leads to a growing number of processing algorithms for mitigation. Analyses of their performance have been mostly based on either simulations or on a small number of exemplary measurements. In this contribution, a corpus of real-world radar measurements is used, to which strongly varying simulated interferences are added. A statistical analysis framework is then applied to a set of state-of-the-art mitigation algorithms, studying the impact of highly complex object environments on their performance.

In this paper a method for interference detection and cancellation for automotive radar systems is proposed. With the growing amount of vehicles equipped with radar sensors, interference mitigation techniques are getting more and more important to maintain good interoperability. Based on the time domain signal of a 76 GHz chirp sequence radar the interfering signals of FMCW radar sensors are identified. This is performed by image processing methods applied to the time-frequency-image. With the maximally stable extremal regions algorithm the interference pattern in the signal is identified. Once the disturbed samples are known they are zeroed. To avoid any ringing effects in the processed radar image the neighborhood of affected samples is smoothed using a raised cosine window. The effectiveness of the proposed method is demonstrated on real world measurements. The method reveals weak scattering centers of the vehicle, which are occluded by interference otherwise.

Radar sensors for automotive applications require extremely high performance figures in terms of measurement accuracy and time. LFMCW radar sensors have the advantages of high range resolution, but it will bring out ghost targets and missed targets in multi-target detection. This paper describes a new waveform combining LFMCW with SFCW pairing is proposed and discusses in detail to demonstrate the technical differences and advantages.

Despite the progress in driving automation, the market introduction of higher-level automation has not yet been achieved. One of the main reasons for this is the effort in safety validation to prove functional safety to the customer. However, virtual testing may compromise this challenge, but the modelling of machine perception and proving its validity has not been solved completely. The present research focuses on a novel modelling approach for automotive radar sensors. Due to the complex high-frequency physics of radars, sensor models for vehicle development are challenging. The presented approach employs a semi-physical modelling approach based on experiments. The selected commercial automotive radar was applied in on-road tests where the ground truth was recorded with a precise measurement system installed in ego and target vehicles. High-frequency phenomena were observed and reproduced in the model on the one hand by using physically based equations such as antenna characteristics and the radar equation. On the other hand, high-frequency effects were statistically modelled using adequate error models derived from the measurements. The model was evaluated with performance metrics developed in previous works and compared to a commercial radar sensor model. Results show that, while keeping real-time performance necessary for X-in-the-loop applications, the model is able to achieve a remarkable fidelity as assessed by probability density functions of the radar point clouds and using the Jensen-Shannon divergence. The model delivers values of radar cross-section for the radar point clouds that correlate well with measurements comparable with the Euro NCAP Global Vehicle Target Validation process. The model outperforms a comparable commercial sensor model.

Automotive radar systems are currently under development for various applications to increase comfort and safety. In this paper a network of distributed radar sensors integrated behind the front bumper of a car is considered and described. Each pulse radar sensor measures the target range with extremely high accuracy and resolution. The two-dimensional target positioning is realized by signal processing techniques applied to the range measurements. Target tracking is most important in multiple-target situations. Therefore, three different tracking algorithms are described, tested and compared in this paper.