Vehicle Safety Improvement through Deep Learning and Mobile Sensing

Peng, Zhe; Gao, Shang; Li, Zecheng; Xiao, Bin; Qian, Yi

doi:10.1109/mnet.2018.1700389

Cited by 64 publications

(30 citation statements)

References 14 publications

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“…i) statistical-based approach, ii) logical approach, iii) outlier-detection approach, and iv) trajectory-based approach. The statistical-based approach is developed emphasizing time-series, prediction, trip detection, quantitative patterns, machine learning [39][40][41][42][43][44][45][46][47][48]. The existing logical-based approaches are reported to consider velocity constraints, reduction of travel distance, and human navigational system [49][50][51].…”

Section: Existing Research Trendsmentioning

confidence: 99%

A comprehensive insight towards Pre-processing Methodologies applied on GPS data

Prabha

Kabadi²

2020

IJECE

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Reliability in the utilization of the Global Positioning System (GPS) data demands a higher degree of accuracy with respect to time and positional information required by the user. However, various extrinsic and intrinsic parameters disrupt the data transmission phenomenon from GPS satellite to GPS receiver which always questions the trustworthiness of such data. Therefore, this manuscript offers a comprehensive insight into the data preprocessing methodologies evolved and adopted by present-day researchers. The discussion is carried out with respect to standard methods of data cleaning as well as diversified existing research-based approaches. The review finds that irrespective of a good number of work carried out to address the problem of data cleaning, there are critical loopholes in almost all the existing studies. The paper extracts open end research problems as well as it also offers an evidential insight using use-cases where it is found that still there is a critical need to investigate data cleaning methods.

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Section: Existing Research Trendsmentioning

confidence: 99%

A comprehensive insight towards Pre-processing Methodologies applied on GPS data

Prabha

Kabadi²

2020

IJECE

View full text Add to dashboard Cite

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“…In this sense, several papers have been combining different types of information to optimize traffic planning. Peng et al [29] discuss the main issues to conduct real-time road safety prediction and propose a new deep learning framework (DeepRSI) to solve this problem. In this work, the authors use public data from authoritative official organizations in New York City, that include urban maps, weather data, holiday event data, GPS trajectories generated, and accident event records.…”

Section: Related Workmentioning

confidence: 99%

“…Table 1 shows a qualitative comparison between BSTS and the literature solutions. It presents a summary of literature solutions highlighting the characteristics that we T r a f f ic f lo w T r a f f ic a c c id e n t P o ll u t io n S a f e t y is s u e s BSTS itsSAFE [9] VNS Systems DIFTOS [37] DeepRSI [29] DIVERT [27] EcoTrec [15] NRR [34] ICARUS [11] SafePaths [16] Crowdsafe [30] SAFER [10] Bold solutions are used to validate our approach consider in this work: architecture re-routing, strategy optimization, re-routing considerations. Additionally, we stand out the solutions used to validate our approach, itsSAFE, VNS Systems, EcoTrec, and SafePaths.…”

Section: Related Workmentioning

confidence: 99%

Better safe than sorry: a vehicular traffic re-routing based on traffic conditions and public safety issues

Souza

Braun

Botega

et al. 2019

J Internet Serv Appl

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Vehicular traffic re-routing is the key to provide better traffic mobility. However, taking into account just traffic-related information to recommend better routes for each vehicle is far from achieving the desired requirements of proper transportation management. In this way, context-aware and multi-objective re-routing approaches will play an important role in traffic management. Yet, most procedures are deterministic and cannot support the strict requirements of traffic management applications, since many vehicles potentially will take the same route, consequently degrading overall traffic efficiency. So, we propose an efficient algorithm named as Better Safe Than Sorry (BSTS), based on Pareto-efficiency. Simulation results have shown that our proposal provides a better trade-off between mobility and safety than state-of-the-art approaches and also avoids the problem of potentially creating different congestion spots.

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“…By extracting features from the collected heterogeneous data and modeling the urban accident risk by situationaware non-negative matrix decomposition, Chen et al [7] proposed a framework to estimate the accident risk by using matrix decomposition method. In order to improve vehicle safety, a new deep learning framework (DeepRSI) [8], which considered the spatio-temporal relationship between vehicle GPS trajectory and external environmental factors, was proposed to predict real-time road safety from the point of view of data mining. Qin et al [9] trained the model on the basis of historical traffic accident data with Bayesian network.…”

Section: Related Workmentioning

confidence: 99%

FMCNN: A Factorization Machine Combined Neural Network for Driving Safety Prediction in Vehicular Communication

et al. 2019

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Traffic accidents have globally increased over the past decades, and the driving safety has become an important issue for human society. Despite great progress, the existing driving safety prediction algorithms hardly consider the sophisticated feature interactions between the driving information, traffic information, and driver information. Therefore, in order to solve this problem, a factorization machine combined neural network (FMCNN) is proposed in this paper to predict the driving safety in vehicular communication. In the proposed framework, the factorization machine and the deep neural network are used to learn the effects of low-order and high-order feature interactions from the driving information and the weather information in the pre-training phase, respectively. After the pre-training phase, the high-order feature interactions extracted by the last hidden layer of the deep neural network and the low-order feature interactions trained by the factorization machine are the input of a new deep neural network to predict the driving safety. Varying from the most machine learning algorithms, the proposed algorithm does not require manual extraction of features. It can automatically extract features from the driving information, traffic information, and driver information collected by the vehicle ad hoc network. The experiment results show that the prediction result of the proposed FMCNN is better than DNN and FM in AUC and Logloss.

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Vehicle Safety Improvement through Deep Learning and Mobile Sensing

Cited by 64 publications

References 14 publications

A comprehensive insight towards Pre-processing Methodologies applied on GPS data

A comprehensive insight towards Pre-processing Methodologies applied on GPS data

Better safe than sorry: a vehicular traffic re-routing based on traffic conditions and public safety issues

FMCNN: A Factorization Machine Combined Neural Network for Driving Safety Prediction in Vehicular Communication

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