Driver Safe Speed Model Based on BP Neural Network for Rural Curved Roads

Chen, Xiaolei; Chi, Ruijuan; Wang, Jianqiang; Pang, Changle; Xiao, Qing

doi:10.1007/978-3-642-27275-2_10

Cited by 10 publications

(5 citation statements)

References 5 publications

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“…However, the trajectory reconstruction is an extremely complex and non‐linear problem due to the complicated spatiotemporal variation of the trajectories. Backpropagation (BP) neural networks have been used widely to solve such problems (Chen, Chi, Wang, Pang, & Xiao, ; Ding, Wang, Wang, & Baumann, ; Partsinevelos, Agouris, & Stefanidis, ; Xu, Li, & Claramunt, ). It was demonstrated in previous studies that BP neural networks have the ability to capture non‐linearity, and good prediction capability and flexibility.…”

Section: Related Workmentioning

confidence: 99%

Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records

Liu

et al. 2018

Transactions in GIS

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Understanding the spatiotemporal dynamics of urban population is crucial for addressing a wide range of urban planning and management issues. Aggregated geospatial big data have been widely used to quantitatively estimate population distribution at fine spatial scales over a given time period. However, it is still a challenge to estimate population density at a fine temporal resolution over a large geographical space, mainly due to the temporal asynchrony of population movement and the challenges to acquiring a complete individual movement record. In this article, we propose a method to estimate hourly population density by examining the time-series individual trajectories, which were reconstructed from call detail records using BP neural networks. We first used BP neural networks to predict the positions of mobile phone users at an hourly interval and then estimated the hourly population density using log-linear regression at the cell tower level. The estimated population density is linearly correlated with population census data at the sub-district level. Trajectory clustering results show five distinct diurnal dynamic patterns of population movement in the study area, revealing spatially explicit characteristics of the diurnal commuting flows, though the driving forces of the flows need further investigation.

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Section: Related Workmentioning

confidence: 99%

Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records

Liu

et al. 2018

Transactions in GIS

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“…However, the parameters in these models are not sufficient to conform to real traffic conditions and driver behavior. To compensate for this shortcoming, Chen et al considered driver behavior in research concerning a backpropagation neural network-based CSWS (14,15). However, they did not distinguish the safe speeds of different vehicle types.…”

Section: Risk Prediction For Curve Speed Warning By Considering Human Vehicle and Road Factorsmentioning

confidence: 99%

Risk Prediction for Curve Speed Warning by Considering Human, Vehicle, and Road Factors

Sun

Chu

et al. 2016

Transportation Research Record: Journal of the Transportation R

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Current curve speed warning systems take into account mostly vehicle and road factors but not driver behavior. The systems aim at detecting sideslips of small cars on curves without consideration of rollovers for vehicles with an elevated center of gravity. In this study, a curve speed model that considers human, vehicle, and road factors is built to prevent not only sideslips but also rollover accidents for vehicles with an elevated center of gravity. In addition, a risk prediction model is presented to judge accident risk levels and determine levels of warning. Finally, the effectiveness of the presented system is validated with one skilled driver who carries out one test through a simulator under different curve scenarios. To verify the system, data from simulator tests were collected for offline checking of the system. The data were used to calculate safe speeds by using the curve speed model and to determine the levels of risk based on the risk prediction model. The results show that the system is highly compatible with the skilled driver in terms of warning accuracy and timing. Specifically, the correct alarm rate (i.e., the driver brakes and the system’s alarm goes off) of the system is 83.57% and the error alarm rate (i.e., the driver does not brake but the system’s alarm goes off) is 9.79%. Moreover, more than 80% of the time the difference between the system warning time and the operating time of the skilled driver is less than 2 s.

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“…Fortunately, the data-driven methods, such as artificial neural networks (ANNs), have been widely used for driving behavior prediction because of their flexible structures and powerful capability to describe non-linearity [18][19][20][21]. For instance, ref.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, ref. [19] used three variables (safe speed, workload, and yaw rate) as the inputs to predict a safe speed in curve negotiation based on a two-layer back propagation (BP) neural network. In addition, the ANNs were also used to predict upcoming lane change behavior by considering three phases including lane change intention, preparation, and action [20].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the ANNs were also used to predict upcoming lane change behavior by considering three phases including lane change intention, preparation, and action [20]. Although significant achievements have been made, the existing ANN-based methods, such as BP and Radial Basis Function (RBF) neural network showed a limited accuracy in driving behavior learning and prediction because of their shallow architecture and manuallyselected features [19,22,23].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Yang

Zhao²,

Lü

et al. 2021

Sensors

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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.

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Driver Safe Speed Model Based on BP Neural Network for Rural Curved Roads

Cited by 10 publications

References 5 publications

Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records

Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records

Risk Prediction for Curve Speed Warning by Considering Human, Vehicle, and Road Factors

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

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