Lateral Road-mark Reconstruction Using Neural Network for Safe Autonomous Driving in Snow-wet Environments

Aldibaja, Mohammad; Kuramoto, Akiuse; Yanase, Reo; Kim, Tae Hyon; Yonada, Keisku; Suganuma, Naoki

doi:10.1109/iisr.2018.8535758

Cited by 3 publications

(2 citation statements)

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“…The study focused on and successfully improved localization accuracy by combining the IEKF and multi-LSTM in the presence of GPS outages, but found out that the longer the GPS outage, the higher the localization error. Another example of using neural networks for lateral localization in harsh weather is shown in [110]. The work employs a neural network to match edge profiles on the road in differing weather conditions, thus assisting in precise lateral localization.…”

Section: A Neural Networkmentioning

confidence: 99%

A Survey on Localization for Autonomous Vehicles

Kumar,

Muhammad

2023

IEEE Access

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Section: A Neural Networkmentioning

confidence: 99%

A Survey on Localization for Autonomous Vehicles

Kumar,

Muhammad

2023

IEEE Access

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“…LiDAR can provide accurate spatial point cloud information, therefore most 3D object detection algorithms [ 1 , 2 , 3 , 4 , 5 ] are based on LiDAR and achieve better detection performance. However, both sensors are susceptible to weather noise: the camera is susceptible to image texture noise and the LiDAR is susceptible to spatial point coordinate noise [ 6 , 7 , 8 , 9 ]. This paper focuses on rainy scenarios, as it is the most common dynamic challenging weather condition that effects vision sensors.…”

Section: Introductionmentioning

confidence: 99%

Anti-Noise 3D Object Detection of Multimodal Feature Attention Fusion Based on PV-RCNN

Zhu

et al. 2022

Sensors

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3D object detection methods based on camera and LiDAR fusion are susceptible to environmental noise. Due to the mismatch of physical characteristics of the two sensors, the feature vectors encoded by the feature layer are in different feature spaces. This leads to the problem of feature information deviation, which has an impact on detection performance. To address this problem, a point-guided feature abstract method is presented to fuse the camera and LiDAR at first. The extracted image features and point cloud features are aggregated to keypoints for enhancing information redundancy. Second, the proposed multimodal feature attention (MFA) mechanism is used to achieve adaptive fusion of point cloud features and image features with information from multiple feature spaces. Finally, a projection-based farthest point sampling (P-FPS) is proposed to downsample the raw point cloud, which can project more keypoints onto the close object and improve the sampling rate of the point-guided image features. The 3D bounding boxes of the object is obtained by the region of interest (ROI) pooling layer and the fully connected layer. The proposed 3D object detection algorithm is evaluated on three different datasets, and the proposed algorithm achieved better detection performance and robustness when the image and point cloud data contain rain noise. The test results on a physical test platform further validate the effectiveness of the algorithm.

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Exploring the challenges and opportunities of image processing and sensor fusion in autonomous vehicles: A comprehensive review

Nahata,

Othman

2023

ELECTRENG

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<abstract> <p>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.</p> </abstract>

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Lateral Road-mark Reconstruction Using Neural Network for Safe Autonomous Driving in Snow-wet Environments

Cited by 3 publications

References 10 publications

A Survey on Localization for Autonomous Vehicles

A Survey on Localization for Autonomous Vehicles

Anti-Noise 3D Object Detection of Multimodal Feature Attention Fusion Based on PV-RCNN

Exploring the challenges and opportunities of image processing and sensor fusion in autonomous vehicles: A comprehensive review

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