Automotive Radar and Lidar Systems for Next Generation Driver Assistance Functions

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.

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.

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.

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.

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.

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.

A production oriented GaAs HEMT MMIC chipset for a 77 GHz FMCW automotive radar system is reported. It differs mainly in two aspects from the GaAs MMIC solutions described earlier: (1) 0.12 /spl mu/m gatelength PHEMTs are fabricated by optical stepper lithography, (2) a coplanar design is used. A fully passivated PHEMT fabrication process is reported with current-gain and power-gain cutoff frequencies exceeding 115 and 220 GHz, respectively. The design and performance of the chipset consisting of four different MMICs (VCO, harmonic mixer, transmitter, receiver) is described. The great potential of this MMIC approach to meet all system requirements of an automotive radar sensor in a cost effective and production oriented way is shown.

Today, a lot of research on autonomous driving technology is being conducted, and various vehicles with autonomous driving functions, such as ACC (adaptive cruise control) are being released. The autonomous vehicle recognizes obstacles ahead by the fusion of data from various sensors, such as lidar and radar sensors, including camera sensors. As the number of vehicles equipped with such autonomous driving functions increases, securing safety and reliability is a big issue. Recently, Mobileye proposed the RSS (responsibility-sensitive safety) model, which is a white box mathematical model, to secure the safety of autonomous vehicles and clarify responsibility in the case of an accident. In this paper, a method of applying the RSS model to a variable focus function camera that can cover the recognition range of a lidar sensor and a radar sensor with a single camera sensor is considered. The variables of the RSS model suitable for the variable focus function camera were defined, the variable values were determined, and the safe distances for each velocity were derived by applying the determined variable values. In addition, as a result of considering the time required to obtain the data, and the time required to change the focal length of the camera, it was confirmed that the response time obtained using the derived safe distance was a valid result.

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

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.

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.

<div class="section abstract"><div class="htmlview paragraph">As a result of the development of Event Data Recorders (EDR) and the recent FMVSS regulation 49 CFR 563, today’s automobiles provide a limited subset of electronic data measurements of a vehicle’s state before and during a crash. Prior to this data, the only information available about the vehicle movements before or during a collision had come from physical evidence (e.g. tire marks), witnesses, aftermarket camera systems on vehicles, and ground-based cameras that were monitoring vehicle traffic or used for security surveillance. Today’s vehicles equipped with Advanced Driver Assistance Systems (ADAS) have vehicle-based sensors that measure information about the environment around a vehicle including other vehicles, pedestrians, and fixed wayside objects. Vehicles equipped with these ADAS systems use primarily radar, lidar, ultrasonic, and/or image sensors either in standalone operation or in combination to establish the range and movement of potential hazardous objects (e.g. other vehicles, poles, and pedestrians) around a vehicle. The data from these sensors is unique because it measures objective environment information surrounding a vehicle, which can play a role in reconstructing and understanding the contributing factors of an accident. As more vehicles become equipped with advanced safety systems and the requisite sensors for automated vehicles, accident investigators will need to become familiar with system functionality, the data that the sensors measure and may record, as well as the limitations of that data in order to effectively and accurately analyze a crash.</div><div class="htmlview paragraph">This paper reviews the sensors in ADAS collision avoidance systems that may be present on current vehicles and how the data from a vehicle based radar may be used to reconstruct a collision. A radar sensor mounted on a host vehicle is experimentally evaluated using stationary objects, and a stationary and moving radar. The results demonstrate how an investigator would use the radar data to recognize a fixed object in the environment and analyze the relative position with respect to the host vehicle. The analysis of this radar data includes the limitations, accuracy, and validation of the particular radar sensor data. In addition, the authors propose in general how data from radars could be used to elucidate the location of fixed objects in the environment surrounding a vehicle for the purpose of understanding an accident.</div></div>

The millimeter waves have large available bandwidth and thus gigabit throughput can be easily achieved for future wireless communication systems. The frequency band has been identified as potentially suitable frequency band for IMT (International Mobile Telecommunications) systems. In this paper, consideration ECC and Huawei relevant paper, the coexistence interference of 71-78 GHz-band international mobile telecommunications (IMT) and automotive radar is analyzed based on actual geographical and ITU-R system characteristic. Specifically, the interferences of IMT system base stations (BS) and users (UE) to automotive radar are studied, considering high frequency band radio propagation clutter loss. This paper presents the Monte Carlo results in terms of the level of compatibility interference power vs. the distances between IMT and automotive radar in the straight trajectory by using Visualyse V7.9 simulation tool. The results show that in some case the IMT BS operating in the band 71-76 GHz may be well compatible with the automotive radar, the interference power value is mainly under protection criterion value. The provided results are useful for design of future 71/80 GHz IMT-2020 (5G) wireless communications systems and automotive radar system.

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.

To meet the prospective demands of intelligent transportation systems (ITS), the Release 14 (Rel-14) and Rel-15 of the Long Term Evolution (LTE) specifications include solutions for enhanced vehicle-to-everything (V2X) communications. While the technical enablers of Rel-14 are suitable for delivering basic safety messages, Rel-15 supports more demanding ITS services with stringent latency and reliability. Starting in Rel-15 and continuing in Rel-16, the 3GPP was developing a novel radio interface for 5G systems, termed the New Radio (NR), which will enable ultra reliable and low latency communications suitable even for the most demanding ITS applications. In this paper, we overview the new V2X-specific features in Rel-15 and Rel-16. Further, we argue that future V2X and automotive radar systems may reuse common equipment, such as millimeter-wave antenna arrays. We finally discuss the vision of joint vehicular communications and radar sensing as well as characterize unified channel access for millimeter-wave vehicular communications and radar sensing.