  This work presents a step towards utilizing incrementally-improving symbolic
perception knowledge of the robot's surroundings for provably correct reactive
control synthesis applied to an autonomous driving problem. Combining abstract
models of motion control and information gathering, we show that
assume-guarantee specifications (a subclass of Linear Temporal Logic) can be
used to define and resolve traffic rules for cautious planning. We propose a
novel representation called symbolic refinement tree for perception that
captures the incremental knowledge about the environment and embodies the
relationships between various symbolic perception inputs. The incremental
knowledge is leveraged for synthesizing verified reactive plans for the robot.
The case studies demonstrate the efficacy of the proposed approach in
synthesizing control inputs even in case of partially occluded environments.
