  Road attributes understanding is extensively researched to support vehicle's
action for autonomous driving, whereas current works mainly focus on urban road
nets and rely much on traffic signs. This paper generalizes the same issue to
the scenes with little or without traffic signs, such as campuses and
residential areas. These scenes face much more individually diverse appearances
while few annotated datasets. To explore these challenges, a weakly-supervised
framework is proposed to infer and learn road affordances without manual
annotation, which includes three attributes of drivable direction, driving
attention 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 affordances
annotation on image, and the second step implements a weakly-supervised network
to learn partial annotation and predict complete road affordances while
testing. Real-world datasets are collected to validate the proposed method
which achieves 88.2%/80.9% accuracy on direction-level and 74.3% /66.7%
accuracy on image-level in familiar and unfamiliar scenes respectively.
