Autonomous Pedestrian Detection in Transit Buses

This project created a proof of concept for an automated pedestrian detection and avoidance system designed for transit buses. The system detects objects up to 12 meters away, calculates the distance from the system using a solid-state LIDAR, and determines if that object is human by passive infrared. This triggers a visual and sound warning. A Xilinx Zynq-SoC utilizing programmable logic and an ARM-based processing system drive data fusion, and an external power unit makes it configurable for transit-buses

Hardware-accelerated data decoding and reconstruction for automotive LiDAR sensors

The automotive industry is facing an unprecedented technological transformation towards fully autonomous vehicles. Optimists predict that, by 2030, cars will be sufficiently reliable, affordable, and common to displace most current human driving tasks. To cope with these trends, autonomous vehicles require reliable perception systems to hear and see all the surroundings, being light detection and ranging (LiDAR) sensors a key instrument for recreating a 3D visualization of the world. However, for a reliable operation, such systems require LiDAR sensors to provide high-resolution 3D representations of the car’s vicinity, which results in millions of data points to be processed in real-time. With this article we propose the ALFA-Pi, a data packet decoder and reconstruction system fully deployed on an embedded reconfigurable hardware platform. By resorting to field-programmable gate array (FPGA) technology, ALFAPi is able to interface different LiDAR sensors at the same time, while providing custom representation outputs to high-level perception systems. By accelerating the LiDAR interface, the proposed system outperforms current software-only approaches, achieving lower latency in the data acquisition and data decoding tasks while reaching high performance ratios

LiDAR: reconfigurable hardware based data acquisition

Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e Computadores (área de especialização em Sistemas Embebidos e Computadores)There is an expected increase in the demand for Advanced Driver-Assistance Systems (ADAS) over the next decade, incited by regulatory and consumer interest in safety applications that protect drivers and reduce accidents [1]. Even though ADAS applications are still beginning, both the OEMs and their suppliers are realizing that they could become one of the essential characteristics differentiating the various automotive brands, consequently, one of their most important revenue sources. Furthermore, the technologies used in ADAS could be used in the future to create fully autonomous vehicles, which are now becoming a major focus of research and development. There are three main sensor solutions used in ADAS. Firstly, there are optical sensors and camera based-solutions. These are the most versatile and cost-efficient solutions. However, they are easily affected by poor weather and other environmental hazards. Furthermore, they require complex software algorithms to recognize objects [1]. The second solution incorporates short and long range Radars for determining the distance, speed, and direction of objects. These sensors work better than the others in adverse weather conditions. Nonetheless there is typically a compromise between the measurement range and angle [1]. The last type of solution involves using LiDAR systems, which use laser pulses to scan the surroundings and generate a complete and precise three-dimensional image of the environment. The LiDAR is less sensitive to light and weather conditions than optical systems and provides the location of the surrounding objects directly. Due to the ever-growing use of ADAS, there is a need to develop a more advanced LiDAR sensor. To answer that need and to overcome some of the limitations of the current LiDAR sensors, the Chassis Systems Control of the Bosch Group is developing an automotive LiDAR, and the current Master’s thesis is integrated in the project. In this Master’s thesis, an Acquisition System for Bosch’s LiDAR sensor was developed. For measuring the Time-of-Flight of the laser pulses of the LiDAR, to do so multiple TDC Peripherals were developed in an FPGA platform. The measurement precision of the developed Acquisition System varies between 232.17 ps and 188.66 ps, with an average precision of 207.47 ps.É expectável que nas próximas décadas exista um aumento na procura das ADAS, potenciado pelos interesses dos reguladores e dos consumidores em aplicações que protejam o condutor e reduzam o número de acidentes. Tanto os OEMs, como os seus fornecedores aperceberam-se que, apesar das ADAS ainda estarem numa fase inicial, podem-se tornar uma característica diferenciadora entre as diversas marcas de automóveis, e por isso, uma das suas principais fontes de rendimento. Além disso, as tecnologias usadas nas ADAS poderão vir a ser utilizadas para criar veículos autónomos, os quais se estão a revelar como um dos principais focos da pesquisa e desenvolvimento. Existem três principais soluções de sensores usadas nas ADAS. Primeiro, existem as soluções baseadas em sensores óticos, que são as soluções mais versáteis e económicas. No entanto, este tipo de soluções é facilmente afetado pelo mau tempo e outros fatores ambientais. Para além do facto de necessitarem o uso de algoritmos complexos para reconhecerem objectos. A segunda solução incorpora o uso de RADARs de longo e curto alcance, com o objetivo de determinar a distância, velocidade e direção dos objetos. Estes sensores são pouco afetados por condições meteorológicas adversas. Porém, existe um compromisso entre o alcance e o ângulo de medição do sensor. A última solução envolve o uso de sistemas de LiDAR. Estes sistemas usam pulsos de laser para examinar meio-envolvente, de modo a gerar uma imagem tridimensional completa do mesmo. O LiDAR é menos sensível à luz e às condições meteorológicas e consegue fornecer diretamente a localização dos objetos à sua volta. Devido à crescente utilização das ADAS, existe a necessidade de desenvolver sensores LiDAR mais avançados. Para suprir essa necessidade e para ultrapassar algumas das limitações dos sensores atuais, a divisão Chassis Systems Control, do grupo Bosch, está atualmente a desenvolver uma solução de um sensor LiDAR para a indústria automóvel, projeto onde se insere esta dissertação. Nesta dissertação foi desenvolvido um Sistema de Aquisição para o sensor LiDAR. Este sistema mede o TOF dos pulsos de laser usado pelo LiDAR. Para isso, vários periféricos de TDC foram desenvolvidos numa FPGA. A precisão de medição do sistema varia entre os 232.17 ps e os 188.66 ps, com um valor médio de 207.47 ps.This work is supported by European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project nº 037902; Funding Reference: POCI-01-0247-FEDER-037902]

Empowering parallel computing with field programmable gate arrays

After more than 30 years, reconfigurable computing has grown from a concept to a mature field of science and technology. The cornerstone of this evolution is the field programmable gate array, a building block enabling the configuration of a custom hardware architecture. The departure from static von Neumannlike architectures opens the way to eliminate the instruction overhead and to optimize the execution speed and power consumption. FPGAs now live in a growing ecosystem of development tools, enabling software programmers to map algorithms directly onto hardware. Applications abound in many directions, including data centers, IoT, AI, image processing and space exploration. The increasing success of FPGAs is largely due to an improved toolchain with solid high-level synthesis support as well as a better integration with processor and memory systems. On the other hand, long compile times and complex design exploration remain areas for improvement. In this paper we address the evolution of FPGAs towards advanced multi-functional accelerators, discuss different programming models and their HLS language implementations, as well as high-performance tuning of FPGAs integrated into a heterogeneous platform. We pinpoint fallacies and pitfalls, and identify opportunities for language enhancements and architectural refinements

Mapping a guided image filter on the HARP reconfigurable architecture using OpenCL

Intel recently introduced the Heterogeneous Architecture Research Platform, HARP. In this platform, the Central Processing Unit and a Field-Programmable Gate Array are connected through a high-bandwidth, low-latency interconnect and both share DRAM memory. For this platform, Open Computing Language (OpenCL), a High-Level Synthesis (HLS) language, is made available. By making use of HLS, a faster design cycle can be achieved compared to programming in a traditional hardware description language. This, however, comes at the cost of having less control over the hardware implementation. We will investigate how OpenCL can be applied to implement a real-time guided image filter on the HARP platform. In the first phase, the performance-critical parameters of the OpenCL programming model are defined using several specialized benchmarks. In a second phase, the guided image filter algorithm is implemented using the insights gained in the first phase. Both a floating-point and a fixed-point implementation were developed for this algorithm, based on a sliding window implementation. This resulted in a maximum floating-point performance of 135 GFLOPS, a maximum fixed-point performance of 430 GOPS and a throughput of HD color images at 74 frames per second

Fast Ground Filtering of Airborne LiDAR Data Based on Iterative Scan-Line Spline Interpolation

Over the last two decades, a wide range of applications have been developed from Light Detection and Ranging (LiDAR) point clouds. Most LiDAR-derived products require the distinction between ground and non-ground points. Because of this, ground filtering its being one of the most studied topics in the literature and robust methods are nowadays available. However, these methods have been designed to work with offline data and they are generally not well suited for real-time scenarios. Aiming to address this issue, this paper proposes an efficient method for ground filtering of airborne LiDAR data based on scan-line processing. In our proposal, an iterative 1-D spline interpolation is performed in each scan line sequentially. The final spline knots of a scan line are taken into account for the next scan line, so that valuable 2-D information is also considered without compromising computational efficiency. Points are labelled into ground and non-ground by analysing their residuals to the final spline. When tested against synthetic ground truth, the method yields a mean kappa value of 88.59% and a mean total error of 0.50%. Experiments with real data also show satisfactory results under visual inspection. Performance tests on a workstation show that the method can process up to 1 million points per second. The original implementation was ported into a low-cost development board to demonstrate its feasibility to run in embedded systems, where throughput was improved by using programmable logic hardware acceleration. Analysis shows that real-time filtering is possible in a high-end board prototype, as it can process the amount of points per second that current lightweight scanners acquire with low-energy consumptionThis work was supported by the Ministry of Education, Culture, and Sport, Government of Spain (Grant Number TIN2016-76373-P), the Consellería de Cultura, Educación e Ordenación Universitaria (accreditation 2016–2019, ED431G/08, and ED431C 2018/2019), and the European Union (European Regional Development Fund—ERDF)S