3 research outputs found
Roadside LiDAR Assisted Cooperative Localization for Connected Autonomous Vehicles
Advancements in LiDAR technology have led to more cost-effective production
while simultaneously improving precision and resolution. As a result, LiDAR has
become integral to vehicle localization, achieving centimeter-level accuracy
through techniques like Normal Distributions Transform (NDT) and other advanced
3D registration algorithms. Nonetheless, these approaches are reliant on
high-definition 3D point cloud maps, the creation of which involves significant
expenditure. When such maps are unavailable or lack sufficient features for 3D
registration algorithms, localization accuracy diminishes, posing a risk to
road safety. To address this, we proposed to use LiDAR-equipped roadside unit
and Vehicle-to-Infrastructure (V2I) communication to accurately estimate the
connected autonomous vehicle's position and help the vehicle when its
self-localization is not accurate enough. Our simulation results indicate that
this method outperforms traditional NDT scan matching-based approaches in terms
of localization accuracy.Comment: Accepted by 2023 International Conference on Intelligent Computing
and its Emerging Application
Exploring the challenges and opportunities of image processing and sensor fusion in autonomous vehicles: A comprehensive review
Autonomous vehicles are at the forefront of future transportation solutions, but their success hinges on reliable perception. This review paper surveys image processing and sensor fusion techniques vital for ensuring vehicle safety and efficiency. The paper focuses on object detection, recognition, tracking, and scene comprehension via computer vision and machine learning methodologies. In addition, the paper explores challenges within the field, such as robustness in adverse weather conditions, the demand for real-time processing, and the integration of complex sensor data. Furthermore, we examine localization techniques specific to autonomous vehicles. The results show that while substantial progress has been made in each subfield, there are persistent limitations. These include a shortage of comprehensive large-scale testing, the absence of diverse and robust datasets, and occasional inaccuracies in certain studies. These issues impede the seamless deployment of this technology in real-world scenarios. This comprehensive literature review contributes to a deeper understanding of the current state and future directions of image processing and sensor fusion in autonomous vehicles, aiding researchers and practitioners in advancing the development of reliable autonomous driving systems