A survey on trajectory-prediction methods for autonomous driving
In order to drive safely in a dynamic environment, autonomous vehicles should be able to
predict the future states of traffic participants nearby, especially surrounding vehicles, similar …
predict the future states of traffic participants nearby, especially surrounding vehicles, similar …
Machine learning for autonomous vehicle's trajectory prediction: A comprehensive survey, challenges, and future research directions
V Bharilya, N Kumar - Vehicular Communications, 2024 - Elsevier
The significant contribution of human errors, accounting for approximately 94%(with a
margin of±2.2%), to road crashes leading to casualties, vehicle damages, and safety …
margin of±2.2%), to road crashes leading to casualties, vehicle damages, and safety …
Human-inspired autonomous driving: A survey
Autonomous vehicles promise to revolutionize society and improve the daily life of many,
making them a coveted aim for a vast research community. To enable complex reasoning in …
making them a coveted aim for a vast research community. To enable complex reasoning in …
Driving behavior modeling using naturalistic human driving data with inverse reinforcement learning
Driving behavior modeling is of great importance for designing safe, smart, and
personalized autonomous driving systems. In this paper, an internal reward function-based …
personalized autonomous driving systems. In this paper, an internal reward function-based …
A human-like trajectory planning method on a curve based on the driver preview mechanism
J Zhao, D Song, B Zhu, Z Sun, J Han… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
With the development of intelligent vehicle technology, many studies have been focused on
developing human-like trajectory planning methods for automated driving systems. Although …
developing human-like trajectory planning methods for automated driving systems. Although …
Bift: A blockchain-based federated learning system for connected and autonomous vehicles
Machine learning (ML) algorithms are essential components in autonomous driving. In most
existing connected and autonomous vehicles (CAVs), a large amount of driving data …
existing connected and autonomous vehicles (CAVs), a large amount of driving data …
An optimized AdaBoost Multi-class support vector machine for driver behavior monitoring in the advanced driver assistance systems
R Sethuraman, S Sellappan, J Shunmugiah… - Expert Systems with …, 2023 - Elsevier
Abstract Advanced Driver Assistance System (ADAS) is a Cyber-Physical System (CPS)
application mainly developed for human–machine interaction. We employ the CPS …
application mainly developed for human–machine interaction. We employ the CPS …
Personalized car-following control based on a hybrid of reinforcement learning and supervised learning
D Song, B Zhu, J Zhao, J Han… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
With the development of intelligent vehicles, more research has focused on achieving
human-like driving. As an important component of intelligent vehicle control, car-following …
human-like driving. As an important component of intelligent vehicle control, car-following …
Driving behavior modeling and characteristic learning for human-like decision-making in highway
C Xu, W Zhao, C Wang, T Cui… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
To make autonomous vehicles consider driver's personalized characteristics, this paper
proposes an integrated model and learning combined (IMLC) algorithm to realize human …
proposes an integrated model and learning combined (IMLC) algorithm to realize human …
What do traffic simulations have to provide for virtual road safety assessment? Human error modeling in traffic simulations
Will Advanced Driving Assistance Systems (ADAS) and Highly Automated Driving (HAD)
perform in the expected manner? Will they actually make road traffic safer, or will they …
perform in the expected manner? Will they actually make road traffic safer, or will they …