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Ride comfort plays an important role in determining the public acceptance of autonomous vehicles (AVs). Many factors, such as road profile, driving speed, and suspension system, influence the ride comfort of AVs. This study proposes a hierarchical framework for improving ride comfort by integrating speed planning and suspension control in a vehicle‐to‐everything environment. Based on safe, comfortable, and efficient speed planning via dynamic programming, a deep reinforcement learning‐based suspension control is proposed to adapt to the changing pavement conditions. Specifically, a deep deterministic policy gradient with external knowledge (EK‐DDPG) algorithm is designed for the efficient self‐adaptation of suspension control strategies. The external knowledge of action selection and value estimation from other AVs are combined into the loss functions of the DDPG algorithm. In numerical experiments …