Deep reinforcement learning in transportation research: A review
Applying and adapting deep reinforcement learning (DRL) to tackle transportation problems
is an emerging interdisciplinary field. While rapidly growing, a comprehensive and synthetic …
is an emerging interdisciplinary field. While rapidly growing, a comprehensive and synthetic …
[HTML][HTML] Lane departure warning systems and lane line detection methods based on image processing and semantic segmentation: A review
W Chen, W Wang, K Wang, Z Li, H Li, S Liu - Journal of traffic and …, 2020 - Elsevier
Recently, the development and application of lane line departure warning systems have
been in the market. For any of the systems, the key part of lane line tracking, lane line …
been in the market. For any of the systems, the key part of lane line tracking, lane line …
A survey on autonomous vehicle control in the era of mixed-autonomy: From physics-based to AI-guided driving policy learning
This paper serves as an introduction and overview of the potentially useful models and
methodologies from artificial intelligence (AI) into the field of transportation engineering for …
methodologies from artificial intelligence (AI) into the field of transportation engineering for …
A survey of deep rl and il for autonomous driving policy learning
Z Zhu, H Zhao - IEEE Transactions on Intelligent Transportation …, 2021 - ieeexplore.ieee.org
Autonomous driving (AD) agents generate driving policies based on online perception
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …
Combining planning and deep reinforcement learning in tactical decision making for autonomous driving
Tactical decision making for autonomous driving is challenging due to the diversity of
environments, the uncertainty in the sensor information, and the complex interaction with …
environments, the uncertainty in the sensor information, and the complex interaction with …
High-level decision making for safe and reasonable autonomous lane changing using reinforcement learning
B Mirchevska, C Pek, M Werling… - 2018 21st …, 2018 - ieeexplore.ieee.org
Machine learning techniques have been shown to outperform many rule-based systems for
the decision-making of autonomous vehicles. However, applying machine learning is …
the decision-making of autonomous vehicles. However, applying machine learning is …
Uncertainties in onboard algorithms for autonomous vehicles: Challenges, mitigation, and perspectives
K Yang, X Tang, J Li, H Wang, G Zhong… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Autonomous driving is considered one of the revolutionary technologies shaping humanity's
future mobility and quality of life. However, safety remains a critical hurdle in the way of …
future mobility and quality of life. However, safety remains a critical hurdle in the way of …
Fail-safe motion planning for online verification of autonomous vehicles using convex optimization
Safe motion planning for autonomous vehicles is a challenging task, since the exact future
motion of other traffic participant is usually unknown. In this article, we present a verification …
motion of other traffic participant is usually unknown. In this article, we present a verification …
Automated lane change strategy using proximal policy optimization-based deep reinforcement learning
Lane-change maneuvers are commonly executed by drivers to follow a certain routing plan,
overtake a slower vehicle, adapt to a merging lane ahead, etc. However, improper lane …
overtake a slower vehicle, adapt to a merging lane ahead, etc. However, improper lane …
Harmonious lane changing via deep reinforcement learning
In this paper, we study how to learn a harmonious deep reinforcement learning (DRL) based
lane-changing strategy for autonomous vehicles without Vehicle-to-Everything (V2X) …
lane-changing strategy for autonomous vehicles without Vehicle-to-Everything (V2X) …