Development and Performance Evaluation of Integrated Automotive Mid-Range Radar System based on ROI preprocessing technique

Radar is a key technology in the safety system of a modern vehicle. Automotive radars are the critical sensors in advanced driver-assistance systems, which are used in adaptive cruise control, collision avoidance, blind spot detection, lane change assistance, and parking assistance. The book covers all the modern radars used in automotive technology. A long-range radar mounted in the front of the vehicle is usually for adaptive cruise control. The medium range radars mounted in the front and rear provide wider coverage than the long-range radars and they can be used for cross traffic alert and lane change assistance. The corner mounted short range radars support parking aid, obstacle/pedestrian detection and blind spot monitoring. In real applications, these radars usually work together to provide more robust detection results. In this book, we also recognize that the future of automotive radars should not only address conventional exterior applications, but also play important roles for interior applications, such as gesture sensing for human-vehicle interaction and driver/passenger vital signs and presence monitoring. The book is aimed at those radar engineers who are working on automotive applications.

Sensor selection is an essential aspect of blind spot detection (BSD) systems. Indeed, we must choose the sensors appropriately to accomplish high accuracy and performance in any driving condition. Each sensor has exclusive properties which are suitable for a few specific circumstances. Therefore, a comprehensive study is warranted to determine the optimum number of sensors and the type of sensors for BSD. Although sensors have some deficiencies which can deteriorate the whole system's performance, a combination of multiple types of sensors together with data fusion methods in most cases can substantially compensate for sensors imperfection. In this paper, we have concentrated on multi-sensor fusion in BSD system and its advantages which cannot be achieved in a single-sensor BSD system. A sensing structure for the BSD system is proposed considering the indispensable factors in BSD coupled with sensors constraints, features, and specifications.

Blind spot detection is an important feature of Advanced Driver Assistance Systems (ADAS). In this paper, we provide a camera-based deep learning method that accurately detects other vehicles in the blind spot, replacing the traditional higher cost solution using radars. The recent breakthrough of deep learning algorithms shows extraordinary performance when applied to many computer vision tasks. Many new convolutional neural network (CNN) structures have been proposed and most of the networks are very deep in order to achieve the state-of-art performance when evaluated with benchmarks. However, blind spot detection, as a real-time embedded system application, requires high speed processing and low computational complexity. Hereby, we propose a novel method that transfers blind spot detection to an image classification task. Subsequently, a series of experiments are conducted to design an efficient neural network by comparing some of the latest deep learning models. Furthermore, we create a dataset with more than 10,000 labeled images using the blind spot view camera mounted on a test vehicle. Finally, we train the proposed deep learning model and evaluate its performance on the dataset.

Over the last decade, new applications have emerged for radar technology in the automotive industry, such as adaptive cruise control, blind spot detection and automatic emergency braking. It is expected that the continued development of radar will unlock new capabilities for autonomous vehicles and safety systems. Indeed, advancements in integrated circuit design have enabled the development of very high-frequency radars with sophisticated signal processing and machine learning techniques. As a consequence, it is now possible with low power, small form factor and low-cost sensors to have rich point cloud data in dense environments. However, many challenges remain that can reduce precision, recall and accuracy of automotive radar, intra- and inter-platform interference or jamming, multi-radar fusion, multipath effects, false targets and detection ambiguity (range, velocity and angle). Many approaches have been developed to address these challenges; massive hybrid MIMO (Multiple-Input Multiple-Output), compressed sensing methods, sparse arrays, PMCW (Phase-Modulated Continuous Wave) Radar, Artificial Intelligence (AI) algorithms and others. Some of these approaches have already been applied to automotive radars, and some likely will be used in the coming years. This chapter will review why radar is unique, the challenges and the state-of-the-art solutions. We will use intuitive and simplified examples to provide the background needed to understand the utility of radar and these techniques.

The interest for mmWave automotive radar systems comes from the booming market for driver assistance systems. The 77–81 GHz band has been allocated for vehicular radar. Such radars can be used for a wide variety of applications such as collision avoidance, adaptive cruise control, lane-change assistance or monitoring the surroundings of the car. The 79 GHz PMCW radar system belongs to this family. It can not only measure the distance but also the velocity of targets. In this paper, we propose an optimized hardware implementation of the correlator bank of a phase-modulated radar system that modulates a 79 GHz carrier with a 1 GSps Pseudo-noise sequence. It leverages an advanced FPGA platform by using the BPS software from BEEcube. After integration of the digital logic FPGA and the analog transceiver, a real target detection test has been performed in the lab to verify the DSP functionality and the specifications of the radar system. The DSP functionality is shown to support the high sampling rate resulting in a range resolution of 0.15 m.

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 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.

Presents a technical explanation of the specific procedure used in designing an automotive laser radar system. Laser radar represents an effective collision avoidance technology that can contribute to improved vehicle and traffic safety. An analysis is given of the problems involved in the practical application of laser radar and ways of overcoming them. The future prospects for automotive laser radar are also discussed. >

The orthogonal frequency-division multiplexing (OFDM) signaling scheme is a promising waveform for future automotive radar systems. However, in its standard implementation, it comes with massive requirements on the hardware required for storing and processing the received OFDM signals. Moreover, one has no full control over the size of the range-Doppler map (RDM). In this work, a novel OFDM receiver signal processing scheme is proposed that impressively reduces the required memory, reduces computational requirements, and gives full control over the size of the RDM. The effects of the proposed receiver processing scheme on the required hardware and the signal-to-noise ratio are discussed.

<div class="section abstract"><div class="htmlview paragraph">The automatic lane change assist system is an intelligent driving assistance technology oriented to traffic safety, which requires trajectory planning of the lane change maneuver based on the lane change decision. A typical scene of lane change for overtaking is selected, where the front vehicle in the same lane and the rear vehicle in the left lane are deemed to be potential dangerous vehicles through the lane change. Lane change trajectory equation is first established according to the general law of steering wheel angle through lane changes. Based on the relative position, velocity and acceleration information of the dangerous vehicles and the lane change vehicle, motions of these surrounding dangerous vehicles are predicted. At the same time, a multi-objective optimization function is established based on the relative longitudinal safety boundary. The objectives are the minimum safety distance, the lane change time and the front wheel angle. The trajectory planning algorithm of the lane change assist based on the model predictive control with multi-objective is then studied. The influence of vehicle speed on trajectory tracking is analyzed in this paper, then the feasibility and robustness of the algorithm is verified. In addition, the complexity of the model is simplified in considering of real-time demand while accuracy is still guaranteed. Simulation and experimental results show that the trajectory planning algorithm based on the model predictive control with multi - objective has greater improvements of those objectives of lane change compared with PID control. The proposed trajectory optimization algorithm can provide reference for the automatic lane change assist system.</div></div>

<div class="section abstract"><div class="htmlview paragraph">Automotive radar and Vehicle to Vehicle (V2V) technology are currently being developed focusing in the safety of the drivers and passengers. The U.S. Department of Transportation's National Highway Traffic Safety Administration (NTHSA) announced that it is going to create a formal path forward for vehicle-to-vehicle communication for light vehicles meaning that NTHSA will start regulatory proposals on how this technology could become mandatory in the future. Automotive short-range radar (SRR) uses the electromagnetic field distribution around a vehicle including reflection from other objects to detect obstacles. If the vehicle is moving the radars can warn the driver to possible impacts and even automatically trigger safety devices such as seat belts or air bags. One of the biggest challenges on the design of SRR is the high frequency of operation which makes it difficult the use numerical simulation due to the small wavelength, leading to electrical large models. SRR works on 24 GHz and 79 GHz bands. The 79 GHz band is considered to be the long-term operating frequency for SRR. The challenges on the design of V2V technology are related to security issues of the system. Since V2V can act in a passive way (alerting the driver only) and in an active way (triggering actions and devices in the vehicle) the security of the information that is being transmitted must be very protected. Nevertheless, this work focuses only on the physical layer of the system, meaning that only the physical devices are going to be detailed. As for V2V, the one of the biggest challenge is to early predict communication between vehicles using simulation due to the large model that would include many vehicles. This work presents the design of an automotive SRR system on silicon technologies and its performance on a vehicle. A V2V example showing the communication among vehicles is also. Lastly, an application including V2V and SRR in a vehicle is demonstrated.</div></div>

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.

We discuss the test requirement and solutions for 76 GHz automotive radar modules. We give an overview of automotive radar systems currently in development and discuss in detail the application of these radars in adaptive cruise control (ACC) systems. We then present Anritsu's radar test system (RTS) which addresses the test and measurement requirements of 76 GHz automotive radars in R&D and production environments. The RTS is used for module and system characterization as well as on-vehicle verification. The RTS provides a moving target simulator (with variable distance, Doppler, and radar cross-section) for testing and calibrating the radar sensors, and it also provides mm-wave signal analysis for power and spectral characterization of the radar.

The COVID-19 pandemic has sparked a debate on the usefulness of performance evaluations, with employees considering factors like work-from-home and hybrid setups as motivators. Employers aim to maximize employee productivity, and they use performance evaluations to assess performance and emphasize company goals. Ugoani (2020) supports this claim, stating that performance appraisals not only review job performance but also bring attention to the company's goals. Open communication between supervisors and employees is crucial during performance evaluations. However, resistance, lack of motivation, and unclear performance dimensions often lead to questions about performance evaluations. This study aims to determine the effectiveness of performance appraisal practices on employee productivity, as employees today prioritize work-family balance and burnout. The study found that most respondents are not satisfied with their company's performance evaluation system and do not believe it positively affects productivity. They also disagree with the communication of performance results and the effectiveness of supervisors providing feedback. They also disagree with the use of rewards like bonuses or pay raises, which do not motivate employees to work more. They also disagree with the promotion of social activities that do not improve productivity. Additionally, many respondents do not communicate negatively about their work, plan it efficiently, or actively participate in meetings and consultations. They also do not take on extra responsibilities or continuously update their skills and knowledge. The study concludes that the BPO company's performance evaluation practices significantly impact employee productivity.

This thesis investigates the relationship between training and performance evaluation for secondary school teachers in Region 6/Guyana.Effective teacher training and robust performance evaluation mechanisms are critical to improving educational quality and student outcomes.This study examines how teacher training programs align with the criteria used in performance evaluations, as well as how that alignment affects teachers' performance.This study investigates the perceived usefulness and fairness of performance evaluation procedures, the perceived efficacy of current training initiatives, and the correlation between participation in particular training programs and subsequent performance evaluation scores using a mixed-methods approach that includes surveys and interviews with secondary school teachers and administrators.Additionally, the study explores the challenges and possibilities of developing a comprehensive, integrated system of teacher development and assessment within the Guyana context.The findings will offer some insights into the strengths and limitations of current approaches, along with recommendations for strengthening their alignment as a means of supporting teacher growth and, consequently, student learning outcomes in Guyanese secondary schools.