OptimusLine: Consistent Road Line Detection Through Time

In the field of autonomous vehicles, the detection of road line markings is a crucial yet versatile component. It provides real-time guidance for navigation and low-level vehicle control, while it also enables the generation of lane-level HD maps. These maps require high precision to provide low-level details to all future map users. At the same time, control-oriented detection pipelines require increased inference frequency and high robustness to be deployed on a safety-critical system. With this work, we present OptimusLine, a versatile line detection pipeline tackling with ease both scenarios. Built around a frame-by-frame transformer-based neural model operating in image segmentation, we show that OptimusLine achieves state-of-the-art performance and analyze its computational impact. To provide robustness to perturbations when deployed on an actual vehicle, OptimusLine introduces a scheme exploiting temporal links between consecutive frames. Enforcing temporal consistency on each new line prediction, OptimusLine can generate more robust line descriptions and produce an estimate of its prediction uncertainty.