Augmented Reality on LiDAR data: Going beyond Vehicle-in-the-Loop for Automotive Software Validation

Thomas Genevois, Jean-Baptiste Horel, A. Renzaglia, C. Laugier
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引用次数: 6

Abstract

Testing and validating advanced automotive software is of paramount importance to guarantee safety and quality. While real-world testing is highly demanding and simulation testing is not reliable, we propose a new augmented reality framework that takes advantage of both environments. This new testing methodology is intended to be a bridge between Vehicle-in-the-Loop and real-world testing. It enables to easily and safely place the whole vehicle and all its software, from perception to control, in realistic test conditions. This framework provides a flexible way to introduce any virtual element in the outputs of the sensors of the vehicle under test. For each modality of sensing, the framework requires a real time augmentation function that preserves real sensor data and enhances them with virtual data. The LiDAR data augmentation function is presented together with its implementation details. Relying on both qualitative and quantitative analysis of experimental results, the representability of tests scenes generated by the augmented reality framework is finally proven.
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基于激光雷达数据的增强现实:超越车辆在环的汽车软件验证
测试和验证先进的汽车软件对于保证安全和质量至关重要。虽然真实世界的测试要求很高,模拟测试不可靠,但我们提出了一个新的增强现实框架,可以利用这两种环境。这种新的测试方法旨在成为车辆在环和实际测试之间的桥梁。它可以轻松安全地将整个车辆及其所有软件,从感知到控制,置于现实的测试条件下。该框架提供了一种灵活的方法,可以在被测车辆的传感器输出中引入任何虚拟元素。对于每一种感知模式,该框架都需要一个实时增强功能,该功能可以保留真实的传感器数据,并用虚拟数据对其进行增强。介绍了激光雷达数据增强功能及其实现细节。通过对实验结果的定性和定量分析,最终证明了增强现实框架生成的测试场景的可表征性。
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