  Autonomous driving systems need to handle complex scenarios such as lane
following, avoiding collisions, taking turns, and responding to traffic
signals. In recent years, approaches based on end-to-end behavioral cloning
have demonstrated remarkable performance in point-to-point navigational
scenarios, using a realistic simulator and standard benchmarks. Offline
imitation learning is readily available, as it does not require expensive hand
annotation or interaction with the target environment, but it is difficult to
obtain a reliable system. In addition, existing methods have not specifically
addressed the learning of reaction for traffic lights, which are a rare
occurrence in the training datasets. Inspired by the previous work on
multi-task learning and attention modeling, we propose a novel multi-task
attention-aware network in the conditional imitation learning (CIL) framework.
This does not only improve the success rate of standard benchmarks, but also
the ability to react to traffic lights, which we show with standard benchmarks.
