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Autonomous vehicles have the potential to revolutionize intelligent transportation by improving traffic safety, increasing energy efficiency, and reducing congestion. In this study, a novel framework termed DeepAD was proposed and validated for decision making in intelligent autonomous driving via deep reinforcement learning. This framework incorporates multiple driving objectives such as efficiency, safety, and comfort to make informed decisions regarding autonomous vehicles (AVs). The decision-making process utilizes the origin–destination information for macrolevel routing and determines microlevel car-following and lane-changing behaviors. The lane-changing behavior is discretized and learned through a deep Q-network, and the continuous car-following behavior is learned through a deep deterministic policy gradient. Comprehensive simulation experiments on a real-world network demonstrated that …