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Machine learning for autonomous vehicle's trajectory prediction: A comprehensive survey, challenges, and future research directions
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 …
Vehicle detection for autonomous driving: A review of algorithms and datasets
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 …
apparent success of autonomous driving technology, we keep working to achieve fully …
Drivellm: Charting the path toward full autonomous driving with large language models
Human drivers instinctively reason with commonsense knowledge to predict hazards in
unfamiliar scenarios and to understand the intentions of other road users. However, this …
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
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 …
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 …
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
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 …
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
Predicting vehicle trajectories is crucial to ensuring automated vehicle operation efficiency
and safety, particularly on congested multi-lane highways. In such dynamic environments, a …
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
Autonomous driving vehicles can effectively improve traffic conditions and promote the
development of intelligent transportation systems. An autonomous vehicle can be divided …
development of intelligent transportation systems. An autonomous vehicle can be divided …
Multi-modal interaction-aware motion prediction at unsignalized intersections
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 …
being one of the key requirements for their full deployment. However, ensuring their safety …
Key Safety Design Overview in AI-driven Autonomous Vehicles
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 …
artificial intelligence software, along with the complex technical challenges they present, it is …