  In autonomous driving, navigation through unsignaled intersections with many
traffic participants moving around is a challenging task. To provide a solution
to this problem, we propose a novel branched network G-CIL for the navigation
policy learning. Specifically, we firstly represent such dynamic environments
as graph-structured data and propose an effective strategy for edge definition
to aggregate surrounding information for the ego-vehicle. Then graph
convolutional neural networks are used as the perception module to capture
global and geometric features from the environment. To generate safe and
efficient navigation policy, we further incorporate it with conditional
imitation learning algorithm, to learn driving behaviors directly from expert
demonstrations. Our proposed network is capable of handling a varying number of
surrounding vehicles and generating optimal control actions (e.g., steering
angle and throttle) according to the given high-level commands (e.g., turn left
towards the global goal). Evaluations on unsignaled intersections with various
traffic densities demonstrate that our end-to-end trainable neural network
outperforms the baselines with higher success rate and shorter navigation time.
