A cognitive advanced driver assistance systems architecture for autonomous-capable electrified vehicles

KP Divakarla, A Emadi, S Razavi - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
Autonomous vehicle industry is making rapid progress in the development of commercial
vehicles with higher levels of autonomy. Although the general advanced driver assistance …

Categorized review of drive simulators and driver behavior analysis focusing on ACT-R architecture in autonomous vehicles

M Cina, AB Rad - Sustainable Energy Technologies and Assessments, 2023 - Elsevier
Driving a vehicle in a safe manner is a highly specialized task that depends on the driver's
cognitive ability, skills, attention, fast processing, interpretation of traffic situations and rules …

Deep Reinforcement Learning-Based Energy-Efficient Decision-Making for Autonomous Electric Vehicle in Dynamic Traffic Environments

J Wu, Z Song, C Lv - IEEE Transactions on Transportation …, 2023 - ieeexplore.ieee.org
Autonomous driving techniques are promising for improving the energy efficiency of
electrified vehicles (EVs) by adjusting driving decisions and optimizing energy requirements …

On the role of intelligent power management strategies for electrified vehicles: A review of predictive and cognitive methods

AM Ali, B Moulik - IEEE Transactions on Transportation …, 2021 - ieeexplore.ieee.org
In light of increasing demands on decarbonized transportation systems, it became
increasingly necessary to meet performance and environmental requirements for …

Perception, information processing and modeling: Critical stages for autonomous driving applications

D Gruyer, V Magnier, K Hamdi, L Claussmann… - Annual Reviews in …, 2017 - Elsevier
Over the last decades, the development of Advanced Driver Assistance Systems (ADAS) has
become a critical endeavor to attain different objectives: safety enhancement, mobility …

Future role of artificial intelligence in advancing transportation electrification

H Lin, Y Yan, Q Cheng - Journal of Intelligent and Connected …, 2023 - ieeexplore.ieee.org
Over the past decade, the rapid evolution of artificial intelligence (AI) has revolutionized
various sectors, including transportation. This discussion explores the transformative …

A deep reinforcement learning framework for eco-driving in connected and automated hybrid electric vehicles

Z Zhu, S Gupta, A Gupta… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Connected and Automated Vehicles (CAVs), in particular those with multiple power sources,
have the potential to significantly reduce fuel consumption and travel time in real-world …

[HTML][HTML] Intelligent energy management control for extended range electric vehicles based on dynamic programming and neural network

L Xi, X Zhang, C Sun, Z Wang, X Hou, J Zhang - Energies, 2017 - mdpi.com
The extended range electric vehicle (EREV) can store much clean energy from the electric
grid when it arrives at the charging station with lower battery energy. Consuming minimum …

Service-oriented real-time energy-optimal regenerative braking strategy for connected and autonomous electrified vehicles

D Kim, JS Eo, KKK Kim - IEEE Transactions on Intelligent …, 2021 - ieeexplore.ieee.org
This paper presents a real-time vehicle speed planning system called the real-time energy-
optimal deceleration planning system (RT-EDPS). Connectivity and autonomous driving …

[HTML][HTML] Model predictive control and deep reinforcement learning based energy efficient eco-driving for battery electric vehicles

K Yeom - Energy Reports, 2022 - Elsevier
Automated self-driving vehicles not only allow of improved energy saving but also better
traffic flow. In particular, with the rapid technological advance of autonomous self-driving …