Recent advancements in end-to-end autonomous driving using deep learning: A survey
End-to-End driving is a promising paradigm as it circumvents the drawbacks associated with
modular systems, such as their overwhelming complexity and propensity for error …
modular systems, such as their overwhelming complexity and propensity for error …
A survey of end-to-end driving: Architectures and training methods
Autonomous driving is of great interest to industry and academia alike. The use of machine
learning approaches for autonomous driving has long been studied, but mostly in the …
learning approaches for autonomous driving has long been studied, but mostly in the …
End-to-end autonomous driving: Challenges and frontiers
The autonomous driving community has witnessed a rapid growth in approaches that
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …
Transfuser: Imitation with transformer-based sensor fusion for autonomous driving
How should we integrate representations from complementary sensors for autonomous
driving? Geometry-based fusion has shown promise for perception (eg, object detection …
driving? Geometry-based fusion has shown promise for perception (eg, object detection …
Multi-modal fusion transformer for end-to-end autonomous driving
How should representations from complementary sensors be integrated for autonomous
driving? Geometry-based sensor fusion has shown great promise for perception tasks such …
driving? Geometry-based sensor fusion has shown great promise for perception tasks such …
nuplan: A closed-loop ml-based planning benchmark for autonomous vehicles
In this work, we propose the world's first closed-loop ML-based planning benchmark for
autonomous driving. While there is a growing body of ML-based motion planners, the lack of …
autonomous driving. While there is a growing body of ML-based motion planners, the lack of …
Multimodal end-to-end autonomous driving
A crucial component of an autonomous vehicle (AV) is the artificial intelligence (AI) is able to
drive towards a desired destination. Today, there are different paradigms addressing the …
drive towards a desired destination. Today, there are different paradigms addressing the …
Multi-modal sensor fusion-based deep neural network for end-to-end autonomous driving with scene understanding
This study aims to improve the performance and generalization capability of end-to-end
autonomous driving with scene understanding leveraging deep learning and multimodal …
autonomous driving with scene understanding leveraging deep learning and multimodal …
A multimodality fusion deep neural network and safety test strategy for intelligent vehicles
Multimodality fusion based on deep neural networks (DNN) is a significant method for
intelligent vehicles. The special characteristics of DNN lead to the issue of AI safety and …
intelligent vehicles. The special characteristics of DNN lead to the issue of AI safety and …
[HTML][HTML] Real-time hybrid multi-sensor fusion framework for perception in autonomous vehicles
There are many sensor fusion frameworks proposed in the literature using different sensors
and fusion methods combinations and configurations. More focus has been on improving …
and fusion methods combinations and configurations. More focus has been on improving …