Computer vision for autonomous vehicles: Problems, datasets and state of the art
Recent years have witnessed enormous progress in AI-related fields such as computer
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …
Explainability of deep vision-based autonomous driving systems: Review and challenges
This survey reviews explainability methods for vision-based self-driving systems trained with
behavior cloning. The concept of explainability has several facets and the need for …
behavior cloning. The concept of explainability has several facets and the need for …
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 …
End-to-end model-free reinforcement learning for urban driving using implicit affordances
M Toromanoff, E Wirbel… - Proceedings of the IEEE …, 2020 - openaccess.thecvf.com
Reinforcement Learning (RL) aims at learning an optimal behavior policy from its own
experiments and not rule-based control methods. However, there is no RL algorithm yet …
experiments and not rule-based control methods. However, there is no RL algorithm yet …
An end-to-end deep neural network for autonomous driving designed for embedded automotive platforms
In this paper, one solution for an end-to-end deep neural network for autonomous driving is
presented. The main objective of our work was to achieve autonomous driving with a light …
presented. The main objective of our work was to achieve autonomous driving with a light …
Learning to drive from simulation without real world labels
Simulation can be a powerful tool for under-standing machine learning systems and
designing methods to solve real-world problems. Training and evaluating methods purely in …
designing methods to solve real-world problems. Training and evaluating methods purely in …
Residual policy learning facilitates efficient model-free autonomous racing
Motion planning for autonomous racing is a challenging task due to the safety requirement
while driving aggressively. Most previous solutions utilize the prior information or depend on …
while driving aggressively. Most previous solutions utilize the prior information or depend on …
A review of end-to-end autonomous driving in urban environments
D Coelho, M Oliveira - Ieee Access, 2022 - ieeexplore.ieee.org
Autonomous driving in urban environments requires intelligent systems that are able to deal
with complex and unpredictable scenarios. Traditional modular approaches focus on …
with complex and unpredictable scenarios. Traditional modular approaches focus on …
DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve genotype–phenotype prediction
Background Genotypes are strongly associated with disease phenotypes, particularly in
brain disorders. However, the molecular and cellular mechanisms behind this association …
brain disorders. However, the molecular and cellular mechanisms behind this association …
Stabilization approaches for reinforcement learning-based end-to-end autonomous driving
S Chen, M Wang, W Song, Y Yang… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Deep reinforcement learning (DRL) has been successfully applied to end-to-end
autonomous driving, especially in simulation environments. However, common DRL …
autonomous driving, especially in simulation environments. However, common DRL …