[HTML][HTML] Model predictive path tracking control for automated road vehicles: A review

P Stano, U Montanaro, D Tavernini, M Tufo… - Annual reviews in …, 2023 - Elsevier
Thanks to their road safety potential, automated vehicles are rapidly becoming a reality. In
the last decade, automated driving has been the focus of intensive automotive engineering …

Autonomous vehicles: Evolution of artificial intelligence and the current industry landscape

D Garikapati, SS Shetiya - Big Data and Cognitive Computing, 2024 - mdpi.com
The advent of autonomous vehicles has heralded a transformative era in transportation,
resha** the landscape of mobility through cutting-edge technologies. Central to this …

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 sha** humanity's
future mobility and quality of life. However, safety remains a critical hurdle in the way of …

[HTML][HTML] Review of collision avoidance and path planning algorithms used in autonomous underwater vehicles

R Kot - Electronics, 2022 - mdpi.com
The rapid technological development of computing power and system operations today
allows for increasingly advanced algorithm implementation, as well as path planning in real …

Review of integrated chassis control techniques for automated ground vehicles

V Skrickij, P Kojis, E Šabanovič, B Shyrokau, V Ivanov - Sensors, 2024 - mdpi.com
Integrated chassis control systems represent a significant advancement in the dynamics of
ground vehicles, aimed at enhancing overall performance, comfort, handling, and stability …

DGInet: Dynamic graph and interaction-aware convolutional network for vehicle trajectory prediction

J An, W Liu, Q Liu, L Guo, P Ren, T Li - Neural Networks, 2022 - Elsevier
This paper investigates vehicle trajectory prediction problems in real traffic scenarios by fully
harnessing the spatio-temporal dependencies between multiple vehicles. The existing GCN …

PNNUAD: Perception neural networks uncertainty aware decision-making for autonomous vehicle

J Liu, H Wang, L Peng, Z Cao… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Most environment perception methods in autonomous vehicles rely on deep neural
networks because of their impressive performance. However, neural networks have black …

[HTML][HTML] An RRT-Dijkstra-based path planning strategy for autonomous vehicles

R Chen, J Hu, W Xu - Applied Sciences, 2022 - mdpi.com
It is challenging to plan paths for autonomous vehicles on half-structured roads because of
the vast planning area and complex environmental constraints. This work aims to plan …

[HTML][HTML] Artificial intelligence and software modeling approaches in autonomous vehicles for safety management: a systematic review

S Abbasi, AM Rahmani - Information, 2023 - mdpi.com
Autonomous vehicles (AVs) have emerged as a promising technology for enhancing road
safety and mobility. However, designing AVs involves various critical aspects, such as …

Search-based task and motion planning for hybrid systems: Agile autonomous vehicles

Z Ajanović, E Regolin, B Shyrokau, H Ćatić… - arxiv preprint arxiv …, 2023 - arxiv.org
To achieve optimal robot behavior in dynamic scenarios we need to consider complex
dynamics in a predictive manner. In the vehicle dynamics community, it is well know that to …