A survey of deep learning applications to autonomous vehicle control
Designing a controller for autonomous vehicles capable of providing adequate performance
in all driving scenarios is challenging due to the highly complex environment and inability to …
in all driving scenarios is challenging due to the highly complex environment and inability to …
Recent advances in motion and behavior planning techniques for software architecture of autonomous vehicles: A state-of-the-art survey
Autonomous vehicles (AVs) have now drawn significant attentions in academic and
industrial research because of various advantages such as safety improvement, lower …
industrial research because of various advantages such as safety improvement, lower …
Comfortable and energy-efficient speed control of autonomous vehicles on rough pavements using deep reinforcement learning
Rough pavements cause ride discomfort and energy inefficiency for road vehicles. Existing
methods to address these problems are time-consuming and not adaptive to changing …
methods to address these problems are time-consuming and not adaptive to changing …
Distributed motion planning for safe autonomous vehicle overtaking via artificial potential field
Autonomous driving of multi-lane vehicle platoons have attracted significant attention in
recent years due to their potential to enhance the traffic-carrying capacity of the roads and …
recent years due to their potential to enhance the traffic-carrying capacity of the roads and …
Stochastic model predictive control with a safety guarantee for automated driving
T Brüdigam, M Olbrich, D Wollherr… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Automated vehicles require efficient and safe planning to maneuver in uncertain
environments. Largely this uncertainty is caused by other traffic participants, eg, surrounding …
environments. Largely this uncertainty is caused by other traffic participants, eg, surrounding …
Scenario understanding and motion prediction for autonomous vehicles—review and comparison
Scenario understanding and motion prediction are essential components for completely
replacing human drivers and for enabling highly and fully automated driving (SAE-Level …
replacing human drivers and for enabling highly and fully automated driving (SAE-Level …
Robust set-invariance based fuzzy output tracking control for vehicle autonomous driving under uncertain lateral forces and steering constraints
This paper is concerned with a new control method for path tracking of autonomous ground
vehicles. We exploit the fuzzy model-based control framework to deal with the time-varying …
vehicles. We exploit the fuzzy model-based control framework to deal with the time-varying …
Active safety control of automated electric vehicles at driving limits: A tube-based MPC approach
To enhance the active safety performance for automated electric vehicles (AEVs) at driving
limits, the collaborative control of four-wheel steering (4WS) and direct yaw-moment control …
limits, the collaborative control of four-wheel steering (4WS) and direct yaw-moment control …
Autonomous overtaking in gran turismo sport using curriculum reinforcement learning
Professional race-car drivers can execute extreme overtaking maneuvers. However, existing
algorithms for autonomous overtaking either rely on simplified assumptions about the …
algorithms for autonomous overtaking either rely on simplified assumptions about the …
Model predictive control for autonomous ground vehicles: a review
This paper reviews model predictive control (MPC) and its wide applications to both single
and multiple autonomous ground vehicles (AGVs). On one hand, MPC is a well-established …
and multiple autonomous ground vehicles (AGVs). On one hand, MPC is a well-established …