A Review on Handicap Sections and Situations to Improve Driving Safety of Automated Vehicles

Roh, Chang-Gyun; Im, I-Jeong

doi:10.3390/su12145509

Cited by 5 publications

(5 citation statements)

References 14 publications

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“…They concluded that adverse weather conditions, such as rainfall and snowfall, affect accidents [ 20 , 21 , 22 , 23 ]. Additionally, based on the studies, another study that derives and analyzes factors affecting self-driving cars’ driving safety also confirmed that bad weather conditions need to be studied first to ensure driving safety [ 24 ].…”

Section: Introductionmentioning

confidence: 91%

Analysis of Impact of Rain Conditions on ADAS

Roh

Kim

2020

Sensors

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Various technologies are being developed to support safe driving. Among them, ADAS, including LDWS, is becoming increasingly common. This driver assistance system aims to create a safe road environment while compensating for the driver’s carelessness. The driver is affected by external environmental factors such as rainfall, snowfall, and bad weather conditions. ADAS is designed to recognize the surrounding situation and enable safe driving by using sensors, but it does not operate normally in bad weather conditions. In this study, we quantitatively measured the effect of bad weather conditions on the actual ADAS function. Additionally, we conducted a vehicle-based driving experiment to suggest an improvement plan for safer driving. In the driving experiment, when the vehicle driving speed was changed in four stages of rainfall, it was confirmed that it affected the View Range value, where the primary variable is the visibility of ADAS. As a result of the analysis, we demonstrated that when the rainfall exceeded a precipitation of 20 mm, the ADAS sensor did not operate, regardless of the vehicle speed. This means that a problem affecting safe driving may occur due to functionality in bad weather situations in which the driver requires ADAS assistance. Therefore, it is necessary to develop a technology that can maintain the minimum ADAS functionality under rainfall conditions and other bad weather conditions.

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Section: Introductionmentioning

confidence: 91%

Analysis of Impact of Rain Conditions on ADAS

Roh

Kim

2020

Sensors

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“…The data for the mixed road driving cycle is ambiguous due to behaviors, obstacle segments caused by environmental factors [41], Therefore, we discard the data of the mixed road driving cycle in this s the three-type classification experiments on the cleaned data, w expressway driving cycle, the suburban road driving cycle, and the u…”

Section: Dataset and Comparison On The Three-classes Of Cleaned Datamentioning

confidence: 99%

“…The data for the mixed road driving cycle is ambiguous due to irregular driving behaviors, obstacle segments caused by environmental factors [41], or driving faults. Therefore, we discard the data of the mixed road driving cycle in this section.…”

Section: Dataset and Comparison On The Three-classes Of Cleaned Datamentioning

confidence: 99%

Recognition of Commercial Vehicle Driving Cycles Based on Multilayer Perceptron Model

2023

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In this paper, we propose a multilayer perceptron-based recognition method for driving cycles of commercial vehicles. Our method solves the problem of identifying the type of driving cycle for commercial vehicles, and improves the efficiency and sustainability of road traffic. We collect driving condition data of 106,200 km long-distance commercial vehicles to validate our method. We pre-proceed six kinds of quantitative features as the data description; these are average speed, gear ratio, and accelerator pedal opening. Our model includes an input layer, hidden layers, and an output layer. The input layer receives and processes the input as low-dimensional features. The hidden layers consist of the feature extraction module and class regression module. The output layer projects extracted features to the classification space and computes the likelihood for each type. We achieve 99.83%, 97.85%, and 99.40% on the recognition accuracy for the expressway driving cycle, the suburban road driving cycle, and the urban road driving cycle, respectively. The experimental results demonstrate that our model achieves better results than the statistical method using Naive Bayes. Moreover, our method utilizes the data more efficiently and thus gains a better generalization performance.

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“…In subsequent research, a substantial number of studies have been based on PilotNet's end-to-end architecture. A combination of visual temporal dependencies of the input data have been considered in [15] and a convolutional long short-term memory (C-LSTM) network has been proposed for steering control. In [16], surround-view cameras were used for end-to-end learning.…”

Section: Related Workmentioning

confidence: 99%

An NN-Based Double Parallel Longitudinal and Lateral Driving Strategy for Self-Driving Transport Vehicles in Structured Road Scenarios

Xiong

Liu

et al. 2021

Sustainability

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Studies on self-driving transport vehicles have focused on longitudinal and lateral driving strategies in automated structured road scenarios. In this study, a double parallel network (DP-Net) combined with longitudinal and lateral strategy networks is constructed for self-driving transport vehicles in structured road scenarios, which is based on a convolutional neural network (CNN) and a long short-term memory network (LSTM). First, in feature extraction and perception, a preprocessing module is introduced that can ensure the effective extraction of visual information under complex illumination. Then, a parallel CNN sub-network is designed that is based on multifeature fusion to ensure better autonomous driving strategies. Meanwhile, a parallel LSTM sub-network is designed, which uses vehicle kinematic features as physical constraints to improve the prediction accuracy for steering angle and speed. The Udacity Challenge II dataset is used as the training set with the proposed DP-Net input requirements. Finally, for the proposed DP-Net, the root mean square error (RMSE) is used as the loss function, the mean absolute error (MAE) is used as the metric, and Adam is used as the optimization method. Compared with competing models such as PilotNet, CgNet, and E2E multimodal multitask network, the proposed DP-Net is more robust in handling complex illumination. The RMSE and MAE values for predicting the steering angle of the E2E multimodal multitask network are 0.0584 and 0.0163 rad, respectively; for the proposed DP-Net, those values are 0.0107 and 0.0054 rad, i.e., 81.7% and 66.9% lower, respectively. In addition, the proposed DP-Net also has higher accuracy in speed prediction. Upon testing the collected SYSU Campus dataset, good predictions are also obtained. These results should provide significant guidance for using a DP-Net to deploy multi-axle transport vehicles.

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A Review on Handicap Sections and Situations to Improve Driving Safety of Automated Vehicles

Cited by 5 publications

References 14 publications

Analysis of Impact of Rain Conditions on ADAS

Analysis of Impact of Rain Conditions on ADAS

Recognition of Commercial Vehicle Driving Cycles Based on Multilayer Perceptron Model

An NN-Based Double Parallel Longitudinal and Lateral Driving Strategy for Self-Driving Transport Vehicles in Structured Road Scenarios

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