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 …

Vehicle detection for autonomous driving: A review of algorithms and datasets

J Karangwa, J Liu, Z Zeng - IEEE Transactions on Intelligent …, 2023 - ieeexplore.ieee.org
Nowadays, vehicles with a high level of automation are being driven everywhere. With the
apparent success of autonomous driving technology, we keep working to achieve fully …

Drivellm: Charting the path toward full autonomous driving with large language models

Y Cui, S Huang, J Zhong, Z Liu, Y Wang… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Human drivers instinctively reason with commonsense knowledge to predict hazards in
unfamiliar scenarios and to understand the intentions of other road users. However, this …

Tum autonomous motorsport: An autonomous racing software for the indy autonomous challenge

J Betz, T Betz, F Fent, M Geisslinger… - Journal of Field …, 2023 - Wiley Online Library
For decades, motorsport has been an incubator for innovations in the automotive sector and
brought forth systems, like, disk brakes or rearview mirrors. Autonomous racing series such …

Automatic parking path planning of tracked vehicle based on improved A* and DWA algorithms

H Yang, X Xu, J Hong - IEEE Transactions on Transportation …, 2022 - ieeexplore.ieee.org
Compared to wheeled vehicles, tracked vehicles have unique advantages in disaster relief
and engineering sites. The working environment of tracked vehicles is mostly in fixed-point …

A safety-enhanced eco-driving strategy for connected and autonomous vehicles: A hierarchical and distributed framework

Q Zhou, B Zhou, S Hu, C Roncoli, Y Wang, J Hu… - … research part C …, 2023 - Elsevier
This paper presents a safety-enhanced eco-driving strategy for connected and autonomous
vehicles (CAVs), which is implemented by a hierarchical and distributed framework. The …

Graph-based interaction-aware multimodal 2d vehicle trajectory prediction using diffusion graph convolutional networks

K Wu, Y Zhou, H Shi, X Li, B Ran - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Predicting vehicle trajectories is crucial to ensuring automated vehicle operation efficiency
and safety, particularly on congested multi-lane highways. In such dynamic environments, a …

[HTML][HTML] A review of deep learning-based vehicle motion prediction for autonomous driving

R Huang, G Zhuo, L **ong, S Lu, W Tian - Sustainability, 2023 - mdpi.com
Autonomous driving vehicles can effectively improve traffic conditions and promote the
development of intelligent transportation systems. An autonomous vehicle can be divided …

Multi-modal interaction-aware motion prediction at unsignalized intersections

V Trentin, A Artuñedo, J Godoy… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Autonomous vehicle technologies have evolved quickly over the last few years, with safety
being one of the key requirements for their full deployment. However, ensuring their safety …

Key Safety Design Overview in AI-driven Autonomous Vehicles

V Vyas, Z Xu - arxiv preprint arxiv:2412.08862, 2024 - arxiv.org
With the increasing presence of autonomous SAE level 3 and level 4, which incorporate
artificial intelligence software, along with the complex technical challenges they present, it is …