  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.

Robotics;Computer Vision and Pattern Recognition;Machine Learning