Design and simulation of automotive radar for autonomous vehicles

Modern automobile technology is pushing towards maximizing road safety, connected vehicles, autonomous vehicles, etc. Automotive RADAR is core sensor technology used for ADAS (Advanced Driver Assistance Technology), ACC (Adaptive Cruise Control), AEB (Automatic Emergency Braking System), traffic assistance, parking aid, and obstacle/pedestrian detection. Despite being inexpensive, RADAR technology provides robust results in harsh conditions such as harsh weather, extreme temperature, darkness, etc. However, the performance of these systems depends on the position of the RADAR and its characteristics like frequency, beamwidth, and bandwidths. Moreover, the characterization of varied materials like layers of paint, polish, primer, or layer of rainwater needs to be analyzed. This performance can be predicted through real-time simulation using advanced FEM software like Altair FEKO&WinProp. These simulations can provide valuable insight into the performance of the system, allowing engineers to optimize the system for specific use cases. For example, simulation can be used to determine the optimal parameters of the RADAR system for a given application. This information can then be used to design and build a physical model or prototype that is optimized for the desired performance. These simulations play a prominent role in determining appropriate data collection and sensor fusion, which reduces the cost and time required for the development of a physical model or prototype. The continued growth and demand for advanced safety features in vehicles further highlight the importance of RADAR technology in modern automobile technology. By accurately characterizing the environment and simulating the system's behavior in real time, engineers can optimize RADAR systems for specific use cases, contributing to safer and more efficient driving experience

Kidnapped Radar: Topological Radar Localisation using Rotationally-Invariant Metric Learning

This paper presents a system for robust, large-scale topological localisation using Frequency-Modulated Continuous-Wave (FMCW) scanning radar. We learn a metric space for embedding polar radar scans using CNN and NetVLAD architectures traditionally applied to the visual domain. However, we tailor the feature extraction for more suitability to the polar nature of radar scan formation using cylindrical convolutions, anti-aliasing blurring, and azimuth-wise max-pooling; all in order to bolster the rotational invariance. The enforced metric space is then used to encode a reference trajectory, serving as a map, which is queried for nearest neighbours (NNs) for recognition of places at run-time. We demonstrate the performance of our topological localisation system over the course of many repeat forays using the largest radar-focused mobile autonomy dataset released to date, totalling 280 km of urban driving, a small portion of which we also use to learn the weights of the modified architecture. As this work represents a novel application for FMCW radar, we analyse the utility of the proposed method via a comprehensive set of metrics which provide insight into the efficacy when used in a realistic system, showing improved performance over the root architecture even in the face of random rotational perturbation.Comment: submitted to the 2020 International Conference on Robotics and Automation (ICRA

DEEP LEARNING FOR OBJECT DETECTION USING RADAR DATA

Recently, Deep learning algorithms are becoming increasingly instrumental in autonomous driving by identifying and acknowledging road entities to ensure secure navigation and decision-making. Autonomous car datasets play a vital role in developing and evaluating perception systems. Nevertheless, the majority of current datasets are acquired using Light Detection and Ranging (LiDAR) and camera sensors. Utilizing deep neural networks yields remarkable outcomes in object recognition, especially when applied to analyze data from cameras and LiDAR sensors which perform poorly under adverse weather conditions such as rain, fog, and snow due to the sensor wavelengths. This paper aims to evaluate the ability to use RADAR dataset for detecting objects in adverse weather conditions, when LiDAR and Cameras may fail to be effective. This paper presents two experiments for object detection using Faster-RCNN architecture with Resnet-50 backbone and COCO evaluation metrics. Experiment 1 is object detection over only one class, while Experiment 2 is object detection over eight classes. The results show that as expected the average precision (AP) of detecting one class is (47.2) which is better than the results from detecting eight classes (27.4). Comparing my results from experiment 1 to the literature results which achieved an overall AP (45.77), my result was slightly better in accuracy than the literature mainly due to hyper-parameters optimization. The outcomes of object detection and recognition based on RADAR indicate the potential effectiveness of RADAR data in automotive applications particularly in adverse weather conditions, where vision and LiDAR may encounter limitations

A Review of Sensor Technologies for Perception in Automated Driving

After more than 20 years of research, ADAS are common in modern vehicles available in the market. Automated Driving systems, still in research phase and limited in their capabilities, are starting early commercial tests in public roads. These systems rely on the information provided by on-board sensors, which allow to describe the state of the vehicle, its environment and other actors. Selection and arrangement of sensors represent a key factor in the design of the system. This survey reviews existing, novel and upcoming sensor technologies, applied to common perception tasks for ADAS and Automated Driving. They are put in context making a historical review of the most relevant demonstrations on Automated Driving, focused on their sensing setup. Finally, the article presents a snapshot of the future challenges for sensing technologies and perception, finishing with an overview of the commercial initiatives and manufacturers alliances that will show future market trends in sensors technologies for Automated Vehicles.This work has been partly supported by ECSEL Project ENABLE- S3 (with grant agreement number 692455-2), by the Spanish Government through CICYT projects (TRA2015- 63708-R and TRA2016-78886-C3-1-R)

A Comparison of FPGA and GPGPU Designs for Bayesian Occupancy Filters

Grid-based perception techniques in the automotive sector based on fusing information from different sensors and their robust perceptions of the environment are proliferating in the industry. However, one of the main drawbacks of these techniques is the traditionally prohibitive, high computing performance that is required for embedded automotive systems. In this work, the capabilities of new computing architectures that embed these algorithms are assessed in a real car. The paper compares two ad hoc optimized designs of the Bayesian Occupancy Filter; one for General Purpose Graphics Processing Unit (GPGPU) and the other for Field-Programmable Gate Array (FPGA). The resulting implementations are compared in terms of development effort, accuracy and performance, using datasets from a realistic simulator and from a real automated vehicle.This work has been partially funded by the Spanish Ministry of Economy and Competitiveness with the National Projects TCAP-AUTO (RTC-2015-3942-4) and NAVEGASE (DPI2014-53525-C3-1-R)

Accurate vehicle location system using RFID, an internet of things approach

Modern infrastructure, such as dense urban areas and underground tunnels, can effectively block all GPS signals, which implies that effective position triangulation will not be achieved. The main problem that is addressed in this project is the design and implementation of an accurate vehicle location system using radio-frequency identification (RFID) technology in combination with GPS and the Global system for Mobile communication (GSM) technology, in order to provide a solution to the limitation discussed above. In essence, autonomous vehicle tracking will be facilitated with the use of RFID technology where GPS signals are non-existent. The design of the system and the results are reflected in this paper. An extensive literature study was done on the field known as the Internet of Things, as well as various topics that covered the integration of independent technology in order to address a specific challenge. The proposed system is then designed and implemented. An RFID transponder was successfully designed and a read range of approximately 31 cm was obtained in the low frequency communication range (125 kHz to 134 kHz). The proposed system was designed, implemented, and field tested and it was found that a vehicle could be accurately located and tracked. It is also found that the antenna size of both the RFID reader unit and RFID transponder plays a critical role in the maximum communication range that can be achieved.The University of Pretoria, South Africahttp://www.mdpi.com/journal/sensorsam2016Electrical, Electronic and Computer Engineerin

Vulnerable road users and connected autonomous vehicles interaction: a survey

There is a group of users within the vehicular traffic ecosystem known as Vulnerable Road Users (VRUs). VRUs include pedestrians, cyclists, motorcyclists, among others. On the other hand, connected autonomous vehicles (CAVs) are a set of technologies that combines, on the one hand, communication technologies to stay always ubiquitous connected, and on the other hand, automated technologies to assist or replace the human driver during the driving process. Autonomous vehicles are being visualized as a viable alternative to solve road accidents providing a general safe environment for all the users on the road specifically to the most vulnerable. One of the problems facing autonomous vehicles is to generate mechanisms that facilitate their integration not only within the mobility environment, but also into the road society in a safe and efficient way. In this paper, we analyze and discuss how this integration can take place, reviewing the work that has been developed in recent years in each of the stages of the vehicle-human interaction, analyzing the challenges of vulnerable users and proposing solutions that contribute to solving these challenges.This work was partially funded by the Ministry of Economy, Industry, and Competitiveness of Spain under Grant: Supervision of drone fleet and optimization of commercial operations flight plans, PID2020-116377RB-C21.Peer ReviewedPostprint (published version

Os desafios éticos e jurídicos no desenvolvimento de veículos autônomos

O presente trabalho tem como proposta analisar os empecilhos éticos e jurídicos enfrentados no desenvolvimento de sistemas de inteligência artificial, no que concerne à tomada de decisão de veículos autônomos. Como é praticamente impossível evitar que veículos, em algum momento, entrem em colisão com outros objetos ou com pessoas ou animais não humanos, fabricantes de veículos autônomos têm de enfrentar o as dificuldades éticas e jurídicas relativas ao desenvolvimento de algoritmos que levem em consideração esse tipo de problema. A pergunta que eles se colocam é sobre como o carro deve reagir na eventualidade de uma colisão. A segurança do motorista deve precedência sobre a seguranças dos passageiros? A segurança do motoristas e passageiros deve ter precedência sobre a segurança de outras pessoas? Será abordado nesta monografia a pergunta sobre até onde essa programação é considerada ética e quais seriam as consequências jurídicas em casos de danos, sejam fatais ou não, uma vez que a legislação atual parece não estar ainda preparada para enfrentar essas questões

Mind the Gap: Developments in Autonomous Driving Research and the Sustainability Challenge

Scientific knowledge on autonomous-driving technology is expanding at a faster-than-ever pace. As a result, the likelihood of incurring information overload is particularly notable for researchers, who can struggle to overcome the gap between information processing requirements and information processing capacity. We address this issue by adopting a multi-granulation approach to latent knowledge discovery and synthesis in large-scale research domains. The proposed methodology combines citation-based community detection methods and topic modeling techniques to give a concise but comprehensive overview of how the autonomous vehicle (AV) research field is conceptually structured. Thirteen core thematic areas are extracted and presented by mining the large data-rich environments resulting from 50 years of AV research. The analysis demonstrates that this research field is strongly oriented towards examining the technological developments needed to enable the widespread rollout of AVs, whereas it largely overlooks the wide-ranging sustainability implications of this sociotechnical transition. On account of these findings, we call for a broader engagement of AV researchers with the sustainability concept and we invite them to increase their commitment to conducting systematic investigations into the sustainability of AV deployment. Sustainability research is urgently required to produce an evidence-based understanding of what new sociotechnical arrangements are needed to ensure that the systemic technological change introduced by AV-based transport systems can fulfill societal functions while meeting the urgent need for more sustainable transport solutions

Proof-of-concept of a single-point Time-of-Flight LiDAR system and guidelines towards integrated high-accuracy timing, advanced polarization sensing and scanning with a MEMS micromirror

Dissertação de mestrado integrado em Engenharia Física (área de especialização em Dispositivos, Microssistemas e Nanotecnologias)The core focus of the work reported herein is the fulfillment of a functional Light Detection and Ranging (LiDAR) sensor to validate the direct Time-of-Flight (ToF) ranging concept and the acquisition of critical knowledge regarding pivotal aspects jeopardizing the sensor’s performance, for forthcoming improvements aiming a realistic sensor targeted towards automotive applications. Hereupon, the ToF LiDAR system is implemented through an architecture encompassing both optical and electronical functions and is subsequently characterized under a sequence of test procedures usually applied in benchmarking of LiDAR sensors. The design employs a hybrid edge-emitting laser diode (pulsed at 6kHz, 46ns temporal FWHM, 7ns rise-time; 919nm wavelength with 5nm FWHM), a PIN photodiode to detect the back-reflected radiation, a transamplification stage and two Time-to-Digital Converters (TDCs), with leading-edge discrimination electronics to mark the transit time between emission and detection events. Furthermore, a flexible modular design is adopted using two separate Printed Circuit Boards (PCBs), comprising the transmitter (TX) and the receiver (RX), i.e. detection and signal processing. The overall output beam divergence is 0.4º×1º and an optical peak power of 60W (87% overall throughput) is realized. The sensor is tested indoors from 0.56 to 4.42 meters, and the distance is directly estimated from the pulses transit time. The precision within these working distances ranges from 4cm to 7cm, reflected in a Signal-to-Noise Ratio (SNR) between 12dB and 18dB. The design requires a calibration procedure to correct systematic errors in the range measurements, induced by two sources: the timing offset due to architecture-inherent differences in the optoelectronic paths and a supplementary bias resulting from the design, which renders an intensity dependence and is denoted time-walk. The calibrated system achieves a mean accuracy of 1cm. Two distinct target materials are used for characterization and performance evaluation: a metallic automotive paint and a diffuse material. This selection is representative of two extremes of actual LiDAR applications. The optical and electronic characterization is thoroughly detailed, including the recognition of a good agreement between empirical observations and simulations in ZEMAX, for optical design, and in a SPICE software, for the electrical subsystem. The foremost meaningful limitation of the implemented design is identified as an outcome of the leading-edge discrimination. A proposal for a Constant Fraction Discriminator addressing sub-millimetric accuracy is provided to replace the previous signal processing element. This modification is mandatory to virtually eliminate the aforementioned systematic bias in range sensing due to the intensity dependency. A further crucial addition is a scanning mechanism to supply the required Field-of-View (FOV) for automotive usage. The opto-electromechanical guidelines to interface a MEMS micromirror scanner, achieving a 46º×17º FOV, with the LiDAR sensor are furnished. Ultimately, a proof-of-principle to the use of polarization in material classification for advanced processing is carried out, aiming to complement the ToF measurements. The original design is modified to include a variable wave retarder, allowing the simultaneous detection of orthogonal linear polarization states using a single detector. The material classification with polarization sensing is tested with the previously referred materials culminating in an 87% and 11% degree of linear polarization retention from the metallic paint and the diffuse material, respectively, computed by Stokes parameters calculus. The procedure was independently validated under the same conditions with a micro-polarizer camera (92% and 13% polarization retention).O intuito primordial do trabalho reportado no presente documento é o desenvolvimento de um sensor LiDAR funcional, que permita validar o conceito de medição direta do tempo de voo de pulsos óticos para a estimativa de distância, e a aquisição de conhecimento crítico respeitante a aspetos fundamentais que prejudicam a performance do sensor, ambicionando melhorias futuras para um sensor endereçado para aplicações automóveis. Destarte, o sistema LiDAR é implementado através de uma arquitetura que engloba tanto funções óticas como eletrónicas, sendo posteriormente caracterizado através de uma sequência de testes experimentais comumente aplicáveis em benchmarking de sensores LiDAR. O design tira partido de um díodo de laser híbrido (pulsado a 6kHz, largura temporal de 46ns; comprimento de onda de pico de 919nm e largura espetral de 5nm), um fotodíodo PIN para detetar a radiação refletida, um andar de transamplificação e dois conversores tempo-digital, com discriminação temporal com threshold constante para marcar o tempo de trânsito entre emissão e receção. Ademais, um design modular flexível é adotado através de duas PCBs independentes, compondo o transmissor e o recetor (deteção e processamento de sinal). A divergência global do feixe emitido para o ambiente circundante é 0.4º×1º, apresentando uma potência ótica de pico de 60W (eficiência de 87% na transmissão). O sensor é testado em ambiente fechado, entre 0.56 e 4.42 metros. A precisão dentro das distâncias de trabalho varia entre 4cm e 7cm, o que se reflete numa razão sinal-ruído entre 12dB e 18dB. O design requer calibração para corrigir erros sistemáticos nas distâncias adquiridas devido a duas fontes: o desvio no ToF devido a diferenças nos percursos optoeletrónicos, inerentes à arquitetura, e uma dependência adicional da intensidade do sinal refletido, induzida pela técnica de discriminação implementada e denotada time-walk. A exatidão do sistema pós-calibração perfaz um valor médio de 1cm. Dois alvos distintos são utilizados durante a fase de caraterização e avaliação performativa: uma tinta metálica aplicada em revestimentos de automóveis e um material difusor. Esta seleção é representativa de dois cenários extremos em aplicações reais do LiDAR. A caraterização dos subsistemas ótico e eletrónico é minuciosamente detalhada, incluindo a constatação de uma boa concordância entre observações empíricas e simulações óticas em ZEMAX e elétricas num software SPICE. O principal elemento limitante do design implementado é identificado como sendo a técnica de discriminação adotada. Por conseguinte, é proposta a substituição do anterior bloco por uma técnica de discriminação a uma fração constante do pulso de retorno, com exatidões da ordem sub-milimétrica. Esta modificação é imperativa para eliminar o offset sistemático nas medidas de distância, decorrente da dependência da intensidade do sinal. Uma outra inclusão de extrema relevância é um mecanismo de varrimento que assegura o cumprimento dos requisitos de campo de visão para aplicações automóveis. As diretrizes para a integração de um micro-espelho no sensor concebido são providenciadas, permitindo atingir um campo de visão de 46º×17º. Conclusivamente, é feita uma prova de princípio para a utilização da polarização como complemento das medições do tempo de voo, de modo a suportar a classificação de materiais em processamento avançado. A arquitetura original é modificada para incluir uma lâmina de atraso variável, permitindo a deteção de estados de polarização ortogonais com um único fotodetetor. A classificação de materiais através da aferição do estado de polarização da luz refletida é testada para os materiais supramencionados, culminando numa retenção de polarização de 87% (tinta metálica) e 11% (difusor), calculados através dos parâmetros de Stokes. O procedimento é independentemente validado com uma câmara polarimétrica nas mesmas condições (retenção de 92% e 13%)