Driven by the demand to develop self-driving cars, automotive radars evolve rapidly at this time. Current high end radars therefore have very little in common with the radars that were developed just a few years ago and are being built into today's car series. Anyway, since at least two decades such sensors are referred to as imaging radars, although the set of possible applications has changed significantly. This study displays the leap in the evolution of automotive imaging radars on the example of the Astyx HiRes radar and elucidates the additional information that becomes available for autonomous driving functions like freespace modelling and target classification. In particular, it will be shown that the image quality of a modern high end radar suffices to apply computer vision techniques that were reserved for optical or light detection and ranging (LIDAR) images until now.

In this work, the authors present results for classification of different classes of targets (car, single and multiple people, bicycle) using automotive radar data and different neural networks. A fast implementation of radar algorithms for detection, tracking, and micro-Doppler extraction is proposed in conjunction with the automotive radar transceiver TEF810X and microcontroller unit SR32R274 manufactured by NXP Semiconductors. Three different types of neural networks are considered, namely a classic convolutional network, a residual network, and a combination of convolutional and recurrent network, for different classification problems across the four classes of targets recorded. Considerable accuracy (close to 100% in some cases) and low latency of the radar pre-processing prior to classification (∼0.55 s to produce a 0.5 s long spectrogram) are demonstrated in this study, and possible shortcomings and outstanding issues are discussed.

Automotive radar is a key component for self-driving cars and advanced driver assistant systems. The major requirements of recent automotive radars are high angular resolution and multiple target detection with the constraints of small size, low power, and low cost. With appropriate transmitter spacing, co-located multiple-input–multiple-output (MIMO) radar can emulate larger aperture arrays, producing the required high angular resolution. However, MIMO radar requires waveforms that are orthogonal in frequency, time, or code domain, and orthogonal waveforms developed for pulse radars are unsuitable for automotive frequency modulated continuous waveform (FMCW) radars. This study proposes a code division multiplexing method for automotive MIMO radars by selecting the combined frequency shift key-linear FMCW waveform. The authors show the performance through simulation and discuss constraints. The proposed method is suitable for automotive radars because not only can high angular resolution be achieved by a small number of arrays, but also multiple targets can be detected with the low sampling rate and computational power.

Automated driving is seen as one of the key technologies that influences and shapes our future mobility. Modern advanced driver assistance systems (ADAS) play a vital role towards achieving this goal of automated driving. Depending on the level of automation, the ADAS takes over the complete or partial control of the movement of the car. Hence, it is mandatory that the system reacts reproducibly and safely in a wide range of possible situations. Especially in complex and potentially dangerous traffic scenarios a test system with the ability to simulate realistic scenarios is required. The authors present an implementation of a vehicle-in-the-loop (ViL) test system which accomplishes these goals in a defined environment. Of the great plenty of sensors stimulated in this context, the radar sensor takes a special position due to its robust and comprehensive information perceiving capability. Stimulating the automotive radar sensor in a ViL environment requires supporting the complex movements of the considered traffic scenarios. For this task, a modular and highly scalable radar target stimulator is necessary, which is capable of stimulating multiple independent moving targets with realistic parameters. The authors are discussing the underlying concepts of the suggested solution and are presenting its performance.

Low-terahertz (THz) (above 150 GHz) radar sensing is one of the promising technologies to provide safe navigation for autonomous cars due to its expected high-resolution imaging capability. It is anticipated that for robust operation at high levels of autonomy the sensor suite should provide a fusion of video and radar data and its efficiency depends on radar ability to deliver a resolution high enough to be compatible with that of the optical image. Performance of low-THz radar, capable to deliver required resolution, is considered in this study, with the focus on reflectivities of pedestrians at frequencies within the low-THz region – 150 and 300 GHz. Backscatter returns are collected in a controlled environment at a number of frequency bands and at different aspect angles. Measurement methodology is presented and several indicators of reflectivities are calculated. Results are compared with values of radar cross-section reported for current automotive frequency standards 24 and 77 GHz. The effect of clothing on reflectivities is also considered in this study.

In this study, two radar sensors with angular diversity are used to recognise dynamic hand gestures by analysing the sparse micro-Doppler signatures. The radar echoes are firstly mapped into the time–frequency domain through the Gaussian-windowed Fourier dictionary at each radar sensor. Then the sparse time–frequency features are extracted via the orthogonal matching pursuit algorithm. Finally, the sparse time–frequency features at two radar sensors are fused and input into the modified-Hausdorff-distance-based nearest neighbour classifier to achieve the dynamic hand gesture recognition. The experimental results based on the measured data under three different experimental scenes demonstrate that (i) the recognition accuracy can be improved by fusing the features extracted at two radar sensors when each radar sensor works well on its own; (ii) the recognition accuracy produced by feature fusion keeps satisfactory even if one of the radar sensors has poor performance, which means that the feature fusion can improve the robustness of the recognition system; and (iii) it would be more helpful if the line-of-sights of the two radar sensors are set to be orthogonal to each other.

In this study, the authors investigate the application of the Doppler beam sharpening (DBS) technique for angular refinement to the emerging area of low-terahertz (THz) radar sensing. Ultimately this is to improve radar image quality in the azimuth plane to complement the excellent range resolution and thus improve object classification in low-THz radar imaging systems for autonomous platforms. The study explains the fundamental theory behind the process of DBS and describes the applicability of DBS to automotive sensing, indicating the potential for synthetic beamwidths of a fraction of a degree. Low-THz DBS was experimentally tested under controlled laboratory conditions, not only to accurately localised target objects in Cartesian space but also to provide unique object imaging at low-THz frequencies with wide azimuthal beamwidth antennas. It was shown that a stationary (i.e. non-scanned) wide beam antenna mounted on a moving platform can deliver imagery at least comparable to that produced by physical beamforming, be that steering arrays or narrow beam scanning antennas as in the experimental case presented.

In this study, the authors propose a novel direction of arrival (DoA) estimation algorithm called ‘multiple-input–multiple-output (MIMO)–monopulse’ by combining the monopulse approach with MIMO radar. Monopulse is fast and accurate angle estimation algorithm, which has been well developed for tracking radar. The application of the monopulse technique on MIMO radar is not much considered before, especially for automotive-radar application, and will be discussed in this study. Conventional methods of monopulse DoA estimation include amplitude and phase comparison monopulse. In this study, to improve the performance of monopulse, they utilise Chebyshev and Zolotarev weighting to synthesise sum and difference patterns. A new visualisation method for monopulse ratio is discussed. Finally, they demonstrate the success of the proposed algorithm by processing real data from a 79 GHz frequency-modulated continuous-wave automotive radar.

This study proposes an original ultra-wideband short-range radar (UWB-SRR) recognition system based on higher-order statistics (HOS) and support vector machines (SVMs). The main purpose of this work is to improve the road safety by implementing these techniques for detection and recognition of the uncovered road users such as pedestrians and cyclists. The combination of HOS and cell-averaging constant false alarm rate (CA-CFAR) radar detector has been proposed and investigated. The results show that a combination of HOS and CA-CFAR promises a good performance for UWB radar detector. The authors have also evaluated the performance of SVM-based target recognition system using normalised radar signature as input features. A total of 1000 signatures have been extracted for each class including pedestrian, cyclist, and car, where 50% of them have been used for the training data and the rest for the validation data. The results show that the SVM gives a good performance for the proposed system, where the recognition rates are up to 96.23, 95.25 and 97.23% for the cyclist, pedestrian and car. In the real testing performance using their scenarios, the system has successfully identified 92.77% of the right cyclist, 90.82% of the right pedestrian and 90.73% of the right car.

In this study, the authors propose a periodic clutter suppression method in iron road structures for automotive radar systems. Specific road structures made of iron such as tunnels, soundproof walls, and guardrails have periodic steel frames that generate unwanted reflected signals called radar clutter. This clutter degrades the detection performance of an automotive radar and leads to misdetection of targets. Therefore, efficient signal processing is required to mitigate the adverse effect of clutter within those iron structures. Since the spacings between steel frames are uniform, beat frequencies corresponding to them also appear at regular intervals. This phenomenon is maintained in each radar scan when a vehicle travels in the iron structures. Thus, they suggest an effective periodic clutter suppression method using the relationship between adjacent radar scans. Their proposed clutter suppression scheme was applied to radar signals obtained from the iron road structures and successfully suppressed the periodic clutter.

Cases of interference between pulsed radars, frequency-modulated continuous wave and noise-modulated automotive radars at frequencies around 24 and 77 GHz are considered, and it is shown that pulse- and frequency-modulated waveforms are very robust to interference from each other if the radar receiver is properly designed. Noise modulations are much harder to handle efficiently. Interference from other sources and the potential for deliberate jamming are also considered.

The study introduces the concept of multiple input–multiple output (MIMO) radar or sonar arrays for short-range, high-resolution sensing in vehicular applications. The use of a MIMO architecture, which is becoming increasingly popular in this field, is selected to reduce the amount of physical elements in the array needed for beamforming, but also to allow signal processing approaches for forming narrow beams in the near-field of the array. The study analytically derives the proposed signal processing approach, and then verifies it via simulated and experimental data in a laboratory environment with scientific equipment assembled for this purpose.

The feasibility of microstrip planar antenna arrays printed on a low-temperature co-fired ceramic (LTCC) substrate for an automotive long-range radar application at the frequency of 77 GHz is investigated. The antenna configuration consists of three receiving (RX) and two transmitting (TX) microstrip patch antennas. The full three-dimensional electromagnetic simulations show, despite the high permittivity and losses of the LTCC substrate, that the achievable impedance matching is wider than 1 GHz and the combination of TX and RX radiation patterns satisfies typical requirements in the azimuthal and elevation planes. Problematic, however, is the reduced antenna gain that can be achieved for a given radiation pattern shape due to the relatively high-dielectric losses.

While automotive radars are driving the development of high-performance technologies for remote sensing, pushing radiofrequency systems to higher frequencies, photonics is gradually changing the approach to micro- and millimetre wave RF generation and distribution. With its unique features, photonics can extend the potential of radars, in particular for ground-based traffic surveillance and on-board automotive applications, enhancing traffic safety and enabling the deployment of smart driver-less vehicles. In fact, microwave photonics offers unprecedented flexibility and stability, such as −113 dBc/Hz (at 100 kHz offset frequency) at 80 GHz, with the capability of generating an extremely broad range of carrier frequencies. Moreover, it can employ signals which span up to several GHz of bandwidth, thus allowing higher precision in target detection and discrimination. This study compares photonic and electronic technologies, and it demonstrates, through simulation results, the benefits of a multiple input, multiple output photonic radar when applied to automotive case-study scenarios.