Accelerated LiDAR data processing algorithm for self-driving cars on the heterogeneous computing platform

IF 1.1 4区计算机科学 Q4 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

IET Computers and Digital Techniques Pub Date : 2020-05-19 DOI:10.1049/iet-cdt.2019.0166

Wei Li, Jun Liang, Yunquan Zhang, Haipeng Jia, Lin Xiao, Qing Li

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Abstract

In recent years, light detection and ranging (LiDAR) has been widely used in the field of self-driving cars, and the LiDAR data processing algorithm is the core algorithm used for environment perception in self-driving cars. At the same time, the real-time performance of the LiDAR data processing algorithm is highly demanding in self-driving cars. The LiDAR point cloud is characterised by its high density and uneven distribution, which poses a severe challenge in the implementation and optimisation of data processing algorithms. In view of the distribution characteristics of LiDAR data and the characteristics of the data processing algorithm, this study completes the implementation and optimisation of the LiDAR data processing algorithm on an NVIDIA Tegra X2 computing platform and greatly improves the real-time performance of LiDAR data processing algorithms. The experimental results show that compared with an Intel® Core™ i7 industrial personal computer, the optimised algorithm improves feature extraction by nearly 4.5 times, obstacle clustering by nearly 3.5 times, and the performance of the whole algorithm by 2.3 times.

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异构计算平台上用于自动驾驶汽车的加速LiDAR数据处理算法

近年来，光探测与测距（LiDAR）在自动驾驶汽车领域得到了广泛应用，而LiDAR数据处理算法是用于自动驾驶汽车环境感知的核心算法。同时，激光雷达数据处理算法的实时性在自动驾驶汽车中要求很高。激光雷达点云的特点是其高密度和不均匀分布，这对数据处理算法的实现和优化提出了严峻挑战。针对激光雷达数据的分布特点和数据处理算法的特点，本研究在NVIDIA Tegra X2计算平台上完成了激光雷达数据处理算法实现和优化，大大提高了激光雷达处理算法的实时性。实验结果表明，与Intel®Core相比™ i7工控机，优化后的算法将特征提取提高了近4.5倍，障碍物聚类提高了近3.5倍，整个算法的性能提高了2.3倍。

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来源期刊

IET Computers and Digital Techniques 工程技术-计算机：理论方法

CiteScore

3.50

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： IET Computers & Digital Techniques publishes technical papers describing recent research and development work in all aspects of digital system-on-chip design and test of electronic and embedded systems, including the development of design automation tools (methodologies, algorithms and architectures). Papers based on the problems associated with the scaling down of CMOS technology are particularly welcome. It is aimed at researchers, engineers and educators in the fields of computer and digital systems design and test. The key subject areas of interest are: Design Methods and Tools: CAD/EDA tools, hardware description languages, high-level and architectural synthesis, hardware/software co-design, platform-based design, 3D stacking and circuit design, system on-chip architectures and IP cores, embedded systems, logic synthesis, low-power design and power optimisation. Simulation, Test and Validation: electrical and timing simulation, simulation based verification, hardware/software co-simulation and validation, mixed-domain technology modelling and simulation, post-silicon validation, power analysis and estimation, interconnect modelling and signal integrity analysis, hardware trust and security, design-for-testability, embedded core testing, system-on-chip testing, on-line testing, automatic test generation and delay testing, low-power testing, reliability, fault modelling and fault tolerance. Processor and System Architectures: many-core systems, general-purpose and application specific processors, computational arithmetic for DSP applications, arithmetic and logic units, cache memories, memory management, co-processors and accelerators, systems and networks on chip, embedded cores, platforms, multiprocessors, distributed systems, communication protocols and low-power issues. Configurable Computing: embedded cores, FPGAs, rapid prototyping, adaptive computing, evolvable and statically and dynamically reconfigurable and reprogrammable systems, reconfigurable hardware. Design for variability, power and aging: design methods for variability, power and aging aware design, memories, FPGAs, IP components, 3D stacking, energy harvesting. Case Studies: emerging applications, applications in industrial designs, and design frameworks.