Virtual reality (VR) is a promising data engine for autonomous driving (AD). However, data fidelity in this paradigm is often degraded by VR inconsistency, for which the existing VR approaches become ineffective, as they ignore the inter-dependency between low-level VR synchronizer designs (i.e., data collector) and high-level VR synthesizer designs (i.e., data processor). This paper presents a seamless virtual reality SVR platform for AD, which mitigates such inconsistency, enabling VR agents to interact with each other in a shared symbiotic world. The crux to SVR is an integrated synchronizer and synthesizer IS2 design, which consists of a drift-aware lidar-inertial synchronizer for VR colocation and a motion-aware deep visual synthesis network for augmented reality image generation. We implement SVR on car-like robots in two sandbox platforms, achieving a cm-level VR colocalization accuracy and 3.2% VR image deviation, thereby avoiding missed collisions or model clippings. Experiments show that the proposed SVR reduces the intervention times, missed turns, and failure rates compared to other benchmarks. The SVR-trained neural network can handle unseen situations in real-world environments, by leveraging its knowledge learnt from the VR space.

翻译：虚拟现实（VR）是自动驾驶（AD）领域一种极具前景的数据引擎。然而，该范式中的数据保真度常因VR不一致性而下降，现有VR方法因忽视了低层VR同步器设计（即数据采集器）与高层VR合成器设计（即数据处理器）之间的相互依赖性而失效。本文提出了一种面向AD的无缝虚拟现实（SVR）平台，该平台缓解了此类不一致性，使VR智能体能够在共享共生世界中相互交互。SVR的核心在于集成同步器与合成器（IS²）设计，其包含用于VR协同定位的漂移感知激光雷达-惯性同步器，以及用于增强现实图像生成的运动感知深度视觉合成网络。我们在两个沙盒平台上将SVR实现于类车机器人，实现了厘米级VR协同定位精度和3.2%的VR图像偏差，从而避免了碰撞遗漏或模型穿透。实验表明，与其他基准相比，所提出的SVR降低了干预次数、错过转弯次数和失败率。通过利用从VR空间习得的知识，经SVR训练的神经网络能够处理真实环境中的未见场景。