  In the past several years, road anomaly segmentation is actively explored in
the academia and drawing growing attention in the industry. The rationale
behind is straightforward: if the autonomous car can brake before hitting an
anomalous object, safety is promoted. However, this rationale naturally calls
for a temporally informed setting while existing methods and benchmarks are
designed in an unrealistic frame-wise manner. To bridge this gap, we contribute
the first video anomaly segmentation dataset for autonomous driving. Since
placing various anomalous objects on busy roads and annotating them in every
frame are dangerous and expensive, we resort to synthetic data. To improve the
relevance of this synthetic dataset to real-world applications, we train a
generative adversarial network conditioned on rendering G-buffers for
photorealism enhancement. Our dataset consists of 120,000 high-resolution
frames at a 60 FPS framerate, as recorded in 7 different towns. As an initial
benchmarking, we provide baselines using latest supervised and unsupervised
road anomaly segmentation methods. Apart from conventional ones, we focus on
two new metrics: temporal consistency and latencyaware streaming accuracy. We
believe the latter is valuable as it measures whether an anomaly segmentation
algorithm can truly prevent a car from crashing in a temporally informed
setting.
