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 …
Interpretable end-to-end urban autonomous driving with latent deep reinforcement learning
Unlike popular modularized framework, end-to-end autonomous driving seeks to solve the
perception, decision and control problems in an integrated way, which can be more …
perception, decision and control problems in an integrated way, which can be more …
Autonomous vehicle control: End-to-end learning in simulated urban environments
H Haavaldsen, M Aasboe, F Lindseth - Symposium of the Norwegian AI …, 2019 - Springer
In recent years, considerable progress has been made towards a vehicle's ability to operate
autonomously. An end-to-end approach attempts to achieve autonomous driving using a …
autonomously. An end-to-end approach attempts to achieve autonomous driving using a …
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
A Tampuu, T Matiisen, M Semikin… - … on Neural Networks …, 2020 - ieeexplore.ieee.org
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 …
A reinforcement learning benchmark for autonomous driving in general urban scenarios
Reinforcement learning (RL) has gained significant interest for its potential to improve
decision and control in autonomous driving. However, current approaches have yet to …
decision and control in autonomous driving. However, current approaches have yet to …
Trajectory-guided control prediction for end-to-end autonomous driving: A simple yet strong baseline
Current end-to-end autonomous driving methods either run a controller based on a planned
trajectory or perform control prediction directly, which have spanned two separately studied …
trajectory or perform control prediction directly, which have spanned two separately studied …
Driving policy transfer via modularity and abstraction
End-to-end approaches to autonomous driving have high sample complexity and are difficult
to scale to realistic urban driving. Simulation can help end-to-end driving systems by …
to scale to realistic urban driving. Simulation can help end-to-end driving systems by …
Intelligent environment enabling autonomous driving
MA Khan - IEEE Access, 2021 - ieeexplore.ieee.org
Automated driving is expected to enormously evolve the transportation industry and
ecosystems. Advancement in communications and sensor technologies have further …
ecosystems. Advancement in communications and sensor technologies have further …
A hierarchical architecture for sequential decision-making in autonomous driving using deep reinforcement learning
M Moghadam, GH Elkaim - arXiv preprint arXiv:1906.08464, 2019 - arxiv.org
Tactical decision making is a critical feature for advanced driving systems, that incorporates
several challenges such as complexity of the uncertain environment and reliability of the …
several challenges such as complexity of the uncertain environment and reliability of the …