Integrating Modular Pipelines with End-to-End Learning: A Hybrid Approach for Robust and Reliable Autonomous Driving Systems

Fernando Santos Osório, Luis Alberto Rosero, Iago Pachêco Gomes, Carlos André Przewodowski, Júnior Anderson Rodrigues da Silva, Denis Fernando Wolf

Abstract:

Autonomous driving navigation relies on diverse approaches, each with advantages and
limitations depending on various factors. For HD maps, modular systems excel, while end-to-end
methods dominate mapless scenarios. However, few leverage the strengths of both. This paper
innovates by proposing a hybrid architecture that seamlessly integrates modular perception and
control modules with data-driven path planning. This innovative design leverages the strengths of
both approaches, enabling a clear understanding and debugging of individual components while
simultaneously harnessing the learning power of end-to-end approaches. Our proposed architecture
achieved first and second place in the 2023 CARLA Autonomous Driving Challenge’s SENSORS
and MAP tracks, respectively. These results demonstrate the architecture’s effectiveness in
both map-based and mapless navigation. We achieved a driving score of 41.56 and the highest route
completion of 86.03 in the MAP track of the CARLA Challenge leaderboard 1, and driving scores of
35.36 and 1.23 in the CARLA Challenge SENSOR track with route completions of 85.01 and 9.55, for,
respectively, leaderboard 1 and 2. The results of leaderboard 2 raised the hybrid architecture to the
first position, winning the edition of the 2023 CARLA Autonomous Driving Competition.