Tactical decision making is a critical feature for advanced driving systems,
that incorporates several challenges such as complexity of the uncertain
environment and reliability of the autonomous system. In this work, we develop
a multi-modal architecture that includes the environmental modeling of ego
surrounding and train a deep reinforcement learning (DRL) agent that yields
consistent performance in stochastic highway driving scenarios. To this end, we
feed the occupancy grid of the ego surrounding into the DRL agent and obtain
the high-level sequential commands (i.e. lane change) to send them to
lower-level controllers. We will show that dividing the autonomous driving
problem into a multi-layer control architecture enables us to leverage the AI
power to solve each layer separately and achieve an admissible reliability
score. Comparing with end-to-end approaches, this architecture enables us to
end up with a more reliable system which can be implemented in actual
self-driving cars.

本研究开发了一个多模态架构，包括自我周围环境的建模，并训练了一个深度强化学习代理，以在随机高速公路驾驶场景中实现一致的性能，将自主驾驶问题分成多层控制结构使我们能够将 AI 的能力分别用于解决每一层，最终实现更可靠的系统，可在实际的自动驾驶汽车中实现。