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Road attributes understanding is extensively researched to support vehicle'saction for autonomous driving, whereas current works mainly focus on urban roadnets and rely much on traffic signs. This paper generalizes the same issue tothe scenes with little or without traffic signs, such as campuses andresidential areas. These scenes face much more individually diverse appearanceswhile few annotated datasets. To explore these challenges, a weakly-supervisedframework is proposed to infer and learn road affordances without manualannotation, which includes three attributes of drivable direction, drivingattention center and remaining distance. The method consists of two steps:affordances inference from trajectory and learning from partially labeled data.The first step analyzes vehicle trajectories to get partial affordancesannotation on image, and the second step implements a weakly-supervised networkto learn partial annotation and predict complete road affordances whiletesting. Real-world datasets are collected to validate the proposed methodwhich achieves 88.2%/80.9% accuracy on direction-level and 74.3% /66.7%accuracy on image-level in familiar and unfamiliar scenes respectively.

Weakly-Supervised Road Affordances Inference and Learning in Scenes without Traffic Signs