  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.
