Deep learning has revolutionized autonomous driving by enabling vehicles to perceive and interpret their surroundings with remarkable accuracy. This progress is attributed to various deep learning models, including Mediated Perception, Behavior Reflex, and Direct Perception, each offering unique advantages and challenges in enhancing autonomous driving capabilities. However, there is a gap in research addressing integrating these approaches and understanding their relevance in diverse driving scenarios. This study introduces three distinct neural network models corresponding to Mediated Perception, Behavior Reflex, and Direct Perception approaches. We explore their significance across varying driving conditions, shedding light on the strengths and limitations of each approach. Our architecture fuses information from the base, future latent vector prediction, and auxiliary task networks, using global routing commands to select appropriate action sub-networks. We aim to provide insights into effectively utilizing diverse modeling strategies in autonomous driving by conducting experiments and evaluations. The results show that the ensemble model performs better than the individual approaches, suggesting that each modality contributes uniquely toward the performance of the overall model. Moreover, by exploring the significance of each modality, this study offers a roadmap for future research in autonomous driving, emphasizing the importance of leveraging multiple models to achieve robust performance.

翻译：深度学习通过使车辆以卓越精度感知和解释周围环境，彻底变革了自动驾驶领域。这一进步归功于多种深度学习模型，包括中介感知、行为反射和直接感知，每种模型在增强自动驾驶能力方面均展现出独特优势与挑战。然而，现有研究在整合这些方法并理解其在不同驾驶场景中的适用性方面存在空白。本研究提出了与中介感知、行为反射和直接感知方法相对应的三种不同神经网络模型。我们探究了这些模型在不同驾驶条件下的重要性，揭示了每种方法的优势与局限性。所提出的架构融合了基网络、未来潜在向量预测网络和辅助任务网络的信息，并利用全局路由指令选择合适的动作子网络。通过实验与评估，我们旨在为自动驾驶中如何有效利用多样化建模策略提供见解。结果表明，集成模型的性能优于单一方法，表明每种模态对整体模型性能均有独特贡献。此外，通过探索每种模态的重要性，本研究为未来自动驾驶研究提供了路线图，强调了利用多模型实现稳健性能的关键价值。