scholarly journals The Effects of Cognitive and Visual Functions of Korean Elderly Taxi Drivers on Safe Driving Behavior

2021 ◽  
Vol Volume 14 ◽  
pp. 465-472
Author(s):  
YeongAe Yang ◽  
HyeJin Lee
Keyword(s):  
Driving Behavior ◽  
Safe Driving ◽  
Taxi Drivers ◽  
Visual Functions ◽  
Korean Elderly

Cloning Safe Driving Behavior for Self-Driving Cars using Convolutional Neural Networks

2019 ◽  
Vol 12 (2) ◽  
pp. 120-127 ◽  
Author(s):  
Wael Farag
Keyword(s):  
Gradient Descent ◽  
Autonomous Driving ◽  
Driving Behavior ◽  
Training Data ◽  
Stochastic Gradient Descent ◽  
Data Set ◽  
Safe Driving ◽  
Processing Pipeline ◽  
Self Driving Cars ◽  
And Training

Background: In this paper, a Convolutional Neural Network (CNN) to learn safe driving behavior and smooth steering manoeuvring, is proposed as an empowerment of autonomous driving technologies. The training data is collected from a front-facing camera and the steering commands issued by an experienced driver driving in traffic as well as urban roads. Methods: This data is then used to train the proposed CNN to facilitate what it is called “Behavioral Cloning”. The proposed Behavior Cloning CNN is named as “BCNet”, and its deep seventeen-layer architecture has been selected after extensive trials. The BCNet got trained using Adam’s optimization algorithm as a variant of the Stochastic Gradient Descent (SGD) technique. Results: The paper goes through the development and training process in details and shows the image processing pipeline harnessed in the development. Conclusion: The proposed approach proved successful in cloning the driving behavior embedded in the training data set after extensive simulations.

scholarly journals Exploring the Influence of E-Hailing Applications on the Taxi Industry—From the Perspective of the Drivers

2021 ◽  
Vol 10 (2) ◽  
pp. 77
Author(s):  
Yitong Gan ◽  
Hongchao Fan ◽  
Wei Jiao ◽  
Mengqi Sun
Keyword(s):  
Driving Behavior ◽  
Urban Traffic ◽  
Behavior Changes ◽  
Transportation Industry ◽  
Trajectory Data ◽  
Long Distance ◽  
Taxi Drivers ◽  
Driving Behaviors ◽  
Taxi Industry ◽  
The Times

In China, the traditional taxi industry is conforming to the trend of the times, with taxi drivers working with e-hailing applications. This reform is of great significance, not only for the taxi industry, but also for the transportation industry, cities, and society as a whole. Our goal was to analyze the changes in driving behavior since taxi drivers joined e-hailing platforms. Therefore, this paper mined taxi trajectory data from Shanghai and compared the data of May 2015 with those of May 2017 to represent the before-app stage and the full-use stage, respectively. By extracting two-trip events (i.e., vacant trip and occupied trip) and two-spot events (i.e., pick-up spot and drop-off spot), taxi driving behavior changes were analyzed temporally, spatially, and efficiently. The results reveal that e-hailing applications mine more long-distance rides and new pick-up locations for drivers. Moreover, driver initiative have increased at night since using e-hailing applications. Furthermore, mobile payment facilities save time that would otherwise be taken sorting out change. Although e-hailing apps can help citizens get taxis faster, from the driver’s perspective, the apps do not reduce their cruising time. In general, e-hailing software reduces the unoccupied ratio of taxis and improves the operating ratio. Ultimately, new driving behaviors can increase the driver’s revenue. This work is meaningful for the formulation of reasonable traffic laws and for urban traffic decision-making.

Driver Agent for Encouraging Safe Driving Behavior for the Elderly

2017 ◽  
Author(s):  
Takahiro Tanaka ◽  
Kazuhiro Fujikake ◽  
Takashi Yonekawa ◽  
Misako Yamagishi ◽  
Makoto Inagami ◽  
...  
Keyword(s):  
The Elderly ◽  
Driving Behavior ◽  
Safe Driving

Safe Driving Behavior Measure

2010 ◽  
Author(s):  
Sherrilene Classen ◽  
Sandra M. Winter ◽  
Craig A. Velozo ◽  
Michel Bédard ◽  
Desiree N. Lanford ◽  
...  
Keyword(s):  
Driving Behavior ◽  
Safe Driving

scholarly journals A driving behavior model evaluation for UBI

2017 ◽  
Vol 1 (3) ◽  
pp. 223-236 ◽  
Author(s):  
Zhishuo Liu ◽  
Qianhui Shen ◽  
Jingmiao Ma
Keyword(s):  
Model Evaluation ◽  
Design Methodology ◽  
Driving Behavior ◽  
Risk Level ◽  
Behavior Model ◽  
Safe Driving ◽  
Content Type ◽  
Scoring Model

Purpose This paper aims to provide a driving behavior scoring model to decide the personalized automobile premium for each driver. Design/methodology/approach Driving behavior scoring model. Findings The driving behavior scoring model could effectively reflect the risk level of driver’s safe driving. Originality/value A driving behavior scoring model for UBI.

scholarly journals Lateral and Longitudinal Driving Behavior Prediction Based on Improved Deep Belief Network

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8498
Author(s):  
Lei Yang ◽  
Chunqing Zhao ◽  
Chao Lu ◽  
Lianzhen Wei ◽  
Jianwei Gong
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Back Propagation ◽  
Front Wheel ◽  
Driving Behavior ◽  
Deep Belief Network ◽  
Intelligent Vehicles ◽  
Support Vector ◽  
Safe Driving ◽  
Belief Network

Accurately predicting driving behavior can help to avoid potential improper maneuvers of human drivers, thus guaranteeing safe driving for intelligent vehicles. In this paper, we propose a novel deep belief network (DBN), called MSR-DBN, by integrating a multi-target sigmoid regression (MSR) layer with DBN to predict the front wheel angle and speed of the ego vehicle. Precisely, the MSR-DBN consists of two sub-networks: one is for the front wheel angle, and the other one is for speed. This MSR-DBN model allows ones to optimize lateral and longitudinal behavior predictions through a systematic testing method. In addition, we consider the historical states of the ego vehicle and surrounding vehicles and the driver’s operations as inputs to predict driving behaviors in a real-world environment. Comparison of the prediction results of MSR-DBN with a general DBN model, back propagation (BP) neural network, support vector regression (SVR), and radical basis function (RBF) neural network, demonstrates that the proposed MSR-DBN outperforms the others in terms of accuracy and robustness.

scholarly journals Preliminary Study for Feasibility of Driver Agent in Actual Car Environment<br/>—Driver Agent for Encouraging Safe Driving Behavior (3)

2020 ◽  
Vol 10 (02) ◽  
pp. 128-143
Author(s):  
Takahiro Tanaka ◽  
Kazuhiro Fujikake ◽  
Yuki Yoshihara ◽  
Nihan Karatas ◽  
Hirofumi Aoki ◽  
...  
Keyword(s):  
Driving Behavior ◽  
Safe Driving ◽  
Preliminary Study
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