Autonomous Vehicles: MMW Radar Backscattering Modeling of Traffic Environment, Vehicular Communication Modeling, and Antenna Designs

77 GHz Millimeter-wave (mmWave) radar serves as an essential component among many sensors required for autonomous navigation. High-fidelity simulation is indispensable for nowadays’ development of advanced automotive radar systems because radar simulation can accelerate the design and testing process and help people to better understand and process the radar data. The main challenge in automotive radar simulation is to simulate the complex scattering behavior of various targets in real time, which is required for sensor fusion with other sensory simulation, e.g. optical image simulation. In this thesis, an asymptotic method based on a fast-wideband physical optics (PO) calculation is developed and applied to get high fidelity radar response of traffic scenes and generate the corresponding radar images from traffic targets. The targets include pedestrians, vehicles, and other stationary targets. To further accelerate the simulation into real time, a physics-based statistical approach is developed. The RCS of targets are fit into statistical distributions, and then the statistical parameters are summarized as functions of range and aspect angles, and other attributes of the targets. For advanced radar with multiple transmitters and receivers, pixelated-scatterer statistical RCS models are developed to represent objects as extend targets and relax the requirement for far-field condition. A real-time radar scene simulation software, which will be referred to as Michigan Automotive Radar Scene Simulator (MARSS), based on the statistical models are developed and integrated with a physical 3D scene generation software (Unreal Engine 4). One of the major challenges in radar signal processing is to detect the angle of arrival (AOA) of multiple targets. A new analytic multiple-sources AOA estimation algorithm that outperforms many well-known AOA estimation algorithms is developed and verified by experiments. Moreover, the statistical parameters of RCS from targets and radar images are used in target classification approaches based on machine learning methods. In realistic road traffic environment, foliage is commonly encountered that can potentially block the line-of-sight link. In the second part of the thesis, a non-line-of-sight (NLoS) vehicular propagation channel model for tree trunks at two vehicular communication bands (5.9 GHz and 60 GHz) is proposed. Both near-field and far-field scattering models from tree trunk are developed based on modal expansion and surface current integral method. To make the results fast accessible and retractable, a macro model based on artificial neural network (ANN) is proposed to fit the path loss calculated from the complex electromagnetic (EM) based methods. In the third part of the thesis, two broadband (bandwidth > 50%) omnidirectional antenna designs are discussed to enable polarization diversity for next-generation communication systems. The first design is a compact horizontally polarized (HP) antenna, which contains four folded dipole radiators and utilizing their mutual coupling to enhance the bandwidth. The second one is a circularly polarized (CP) antenna. It is composed of one ultra-wide-band (UWB) monopole, the compact HP antenna, and a dedicatedly designed asymmetric power divider based feeding network. It has about 53% overlapping bandwidth for both impedance and axial ratio with peak RHCP gain of 0.9 dBi.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163001/1/caixz_1.pd

Road Surface Recognition at mm-Wavelengths Using a Polarimetric Radar

We demonstrate detection of ice formations on a road surface using a polarimetric radar operating at 87.5-92.5 GHz. The radar measures the scattering parameters of the surface at horizontal and vertical polarizations and their cross-polarization components. We demonstrate detection of ice for radar beam directed at up to 45⁰ angle of incidence with respect to the surface which allows for road surface characterization in front of a vehicle. The method used is based on a statistical approach where the 2-port scattering parameters are measured multiple times and used to calculate an average scatter coherence matrix representing the surface. The coherence matrix is then decomposed to eigenvalues/vectors, which are used to estimate polarimetric attributes such as target entropy(degree of randomness) and polarimetric pedestal (degree of depolarization). Through measurements of dry, ice-covered and wet road surfaces, we show that both entropy and depolarization are increased with respect to dry surface when a thin ice layer is formed, while their value decrease for the case of wet surface. It is also shown that these polarimetric attributes are not sensitive to surface roughness in dry conditions, minimizing the probability of false alarm due to road surface wear

Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles

Radar-based road user classification is an important yet still challenging task towards autonomous driving applications. The resolution of conventional automotive radar sensors results in a sparse data representation which is tough to recover by subsequent signal processing. In this article, classifier ensembles originating from a one-vs-one binarization paradigm are enriched by one-vs-all correction classifiers. They are utilized to efficiently classify individual traffic participants and also identify hidden object classes which have not been presented to the classifiers during training. For each classifier of the ensemble an individual feature set is determined from a total set of 98 features. Thereby, the overall classification performance can be improved when compared to previous methods and, additionally, novel classes can be identified much more accurately. Furthermore, the proposed structure allows to give new insights in the importance of features for the recognition of individual classes which is crucial for the development of new algorithms and sensor requirements.Comment: 8 pages, 9 figures, accepted paper for 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France, June 201

A high speed Tri-Vision system for automotive applications

Purpose: Cameras are excellent ways of non-invasively monitoring the interior and exterior of vehicles. In particular, high speed stereovision and multivision systems are important for transport applications such as driver eye tracking or collision avoidance. This paper addresses the synchronisation problem which arises when multivision camera systems are used to capture the high speed motion common in such applications. Methods: An experimental, high-speed tri-vision camera system intended for real-time driver eye-blink and saccade measurement was designed, developed, implemented and tested using prototype, ultra-high dynamic range, automotive-grade image sensors specifically developed by E2V (formerly Atmel) Grenoble SA as part of the European FP6 project – sensation (advanced sensor development for attention stress, vigilance and sleep/wakefulness monitoring). Results : The developed system can sustain frame rates of 59.8 Hz at the full stereovision resolution of 1280 × 480 but this can reach 750 Hz when a 10 k pixel Region of Interest (ROI) is used, with a maximum global shutter speed of 1/48000 s and a shutter efficiency of 99.7%. The data can be reliably transmitted uncompressed over standard copper Camera-Link® cables over 5 metres. The synchronisation error between the left and right stereo images is less than 100 ps and this has been verified both electrically and optically. Synchronisation is automatically established at boot-up and maintained during resolution changes. A third camera in the set can be configured independently. The dynamic range of the 10bit sensors exceeds 123 dB with a spectral sensitivity extending well into the infra-red range. Conclusion: The system was subjected to a comprehensive testing protocol, which confirms that the salient requirements for the driver monitoring application are adequately met and in some respects, exceeded. The synchronisation technique presented may also benefit several other automotive stereovision applications including near and far-field obstacle detection and collision avoidance, road condition monitoring and others.Partially funded by the EU FP6 through the IST-507231 SENSATION project.peer-reviewe

Towards an Advanced Automotive Radar Front-end Based on Gap Waveguide Technology

This thesis presents the early works on dual circularly polarized array antenna based on gap waveguide, also microstrip-to-waveguide transitions for integration of automotive radar front-end. Being the most widely used radar antenna, PCB antenna suffers from dielectric loss and design flexibility. Next generation automotive radars demand sophisticated antenna systems with high efficiency, which makes waveguide antenna become a better candidate. Over the last few years, gap waveguide has shown advantages for implementation of complicated antenna systems. Ridge gap waveguides have been widely used in passive gap waveguide components design including slot arrays. In this regard, two transitions between ridge gap waveguides and microstrip lines are presented for the integration with gap waveguide antennas. The transitions are verified in both passive and active configuration. Another work on packaging techniques is presented for integration with inverted microstrip gap waveguide antennas.Systems utilizing individual linear polarization (LP) that lack polarimetric capabilities are not capable of measuring the full scattering matrix, thus losing information about the scenery. To develop a more advanced radar system with better detectability, dual circularly polarized gap waveguide slot arrays for polarimetric radar sensing are investigated. An 8 78 planar array using double grooved circular waveguide polarizer is presented. The polarizers are compact in size and have excellent polarization properties. Multi-layer design of the array antenna benefits from the gap waveguide technology and features better performance. The works presented in this thesis laid the foundation of future works regarding integration of the radar front end. More works on prototyping radar systems using gap waveguide technology will be presented in future publications

Dual-Polarised Radiometer for Road Surface Characterisation

This paper presents measurements using a dual-polarised radiometer operating at 93\ua0GHz to detect ice or water on asphalt in laboratory conditions. The brightness temperatures of both H and V polarizations were measured for a dry surface, liquid water, and ice on asphalt at observation angles of 50\ub0 and 56\ub0. The results presented in this paper demonstrate that the studied road conditions can be identified by the radiometer. The measurements are compared with a model and surface parameters, such as dielectric constant and roughness are fitted and compared to reference values. The experiments and results, described in this article, are the first steps towards the future installation of a polarimetric sensor on a moving vehicle for traffic safety

Integrating Millimeter Wave Radar with a Monocular Vision Sensor for On-Road Obstacle Detection Applications

This paper presents a systematic scheme for fusing millimeter wave (MMW) radar and a monocular vision sensor for on-road obstacle detection. As a whole, a three-level fusion strategy based on visual attention mechanism and driver’s visual consciousness is provided for MMW radar and monocular vision fusion so as to obtain better comprehensive performance. Then an experimental method for radar-vision point alignment for easy operation with no reflection intensity of radar and special tool requirements is put forward. Furthermore, a region searching approach for potential target detection is derived in order to decrease the image processing time. An adaptive thresholding algorithm based on a new understanding of shadows in the image is adopted for obstacle detection, and edge detection is used to assist in determining the boundary of obstacles. The proposed fusion approach is verified through real experimental examples of on-road vehicle/pedestrian detection. In the end, the experimental results show that the proposed method is simple and feasible