Driving Style Identification Algorithm with Real-World Data Based on Statistical Approach

Ouali, Tarek; Shah, Nirav; Kim, Bill; Fuente, David de la; Gao, Bo

doi:10.4271/2016-01-1422

Cited by 11 publications

(12 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a similar way, Figure 7 presents the decision matrix for the driving style algorithms. For this feature, there exist several approaches based on heuristic methods [ 1 , 3 ], artificial intelligence algorithms [ 16 , 41 , 58 ], statistical approaches [ 14 ] and phenomenological models [ 58 ]. Smartphone data (mainly IMU and GPS), OBD readings, vision systems, wheel encoders, and radars are common signals used in algorithms to experimentally determine the driving style.…”

Section: Resultsmentioning

confidence: 99%

“…These events then are used to categorize the driving style as aggressive or non-aggressive . As second option, the statistical algorithm proposed by [ 14 ] is a candidate for the driving style monitoring computations due to its simplicity to be implemented. In this case, the driving style is categorized as calm , normal , and aggressive mode.…”

Section: Resultsmentioning

confidence: 99%

“…Phenomenological algorithms are used to calculate energy expenditure [ 10 , 11 ], or emissions [ 12 ]. Statistical algorithms and modeling have also been used when calculating driving events and energy consumption [ 13 , 14 , 15 ]. More recently, machine learning algorithms are being used to explore the possibility of obtaining better and more robust models for ADAS implementation, e.g., [ 16 ] uses decision trees and neural networks to identify four types of driving style; authors in [ 3 , 17 , 18 ] make use of fuzzy logic in combination with other machine learning algorithms to improve the event detection of risk maneuvers.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Vehicle and Driver Monitoring System Using On-Board and Remote Sensors

Campos-Ferreira

Lozoya-Santos

Tudón-Martínez

et al. 2023

Sensors

View full text Add to dashboard Cite

This paper presents an integrated monitoring system for the driver and the vehicle in a single case of study easy to configure and replicate. On-board vehicle sensors and remote sensors are combined to model algorithms for estimating polluting emissions, fuel consumption, driving style and driver’s health. The main contribution of this paper is the analysis of interactions among the above monitored features highlighting the influence of the driver in the vehicle performance and vice versa. This analysis was carried out experimentally using one vehicle with different drivers and routes and implemented on a mobile application. Compared to commercial driver and vehicle monitoring systems, this approach is not customized, uses classical sensor measurements, and is based on simple algorithms that have been already proven but not in an interactive environment with other algorithms. In the procedure design of this global vehicle and driver monitoring system, a principal component analysis was carried out to reduce the variables used in the training/testing algorithms with objective to decrease the transfer data via Bluetooth between the used devices: a biometric wristband, a smartphone and the vehicle’s central computer. Experimental results show that the proposed vehicle and driver monitoring system predicts correctly the fuel consumption index in 84%, the polluting emissions 89%, and the driving style 89%. Indeed, interesting correlation results between the driver’s heart condition and vehicular traffic have been found in this analysis.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Vehicle and Driver Monitoring System Using On-Board and Remote Sensors

Campos-Ferreira

Lozoya-Santos

Tudón-Martínez

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Each driving style has its own "traits" related to the operation of the vehicle. There are many driving style classi ications [23][24][25]. We have chosen a classi ication based on D örr et al [25] approach, which considers three style types: sporty, normal, and calm.…”

Section: Driving Style Parametersmentioning

confidence: 99%

CarEs: An Emotional Model of a Car With the Stress Factor

Negrin¹,

Barosan²,

Chouhan³

2022

JSEAS

View full text Add to dashboard Cite

Autonomous driving is a growing research ield, that still has many challenges. The main challenges are related with decision-making algorithms, human-machine interaction and acceptance in the technology. Also, the absence of human drivers in autonomous vehicles creates a gap between users and pedestrians interacting with the vehicle. This article aims to de ine vehicle awareness, that eases the collaboration with users to improve safety and have a more human-like driving to increase the technology acceptance. In addition, our approach can be extended to express vehicle social awareness towards the pedestrians and road users. Our approach is based on affective computing. Affective computing is a tool to grant computers to genuinely become intelligent and interact better with humans. Moreover, one of its components is the generation of emotions, of which two of the most important elements are cognitive emotions and primary emotions. The article's objective is to design the model of a primary emotion component, based on safety and that can be personalized depending on the driving style of the user. This component is called the stress factor. The stress factor is correlated with the probability of an accident. The vehicle stress factors contain parameters that can be personalized as a function of a driving style. The stress factor is then attached to an existing cognitive emotion system (CarE) in the automotive domain which we called CarEs. The results of the system behavior showed promising results. The stress factor showed to be useful as a safety indicator. Also, the stress factor can be personalized with the vehicle operation state component. In conclusion, the new system known as CarEs generates vehicle awareness, by improving the vehicle's collaboration with the driver. The collaboration has a positive impact on the vehicle's safety and comfort, and people's reliance on automated vehicles.

show abstract

“…Ouali, T et al [8] evaluated the driving style score through the speed, accelerator pedal position, brake pressure, lateral acceleration, longitudinal acceleration, steering angle, and cruise control signals in CAN bus, and divided the driving style into three categories: calm, normal, or aggressive. This research method needs predefined rules to classify driving styles, so it has a strong dependence on professional knowledge, and relates to the subjective evaluation of the driver.…”

Section: Driving Style Recognition Mehtodmentioning

confidence: 99%

Driving Style Recognition Model Based on NEV High-Frequency Big Data and Joint Distribution Feature Parameters

Xia

Kang

2021

WEVJ

View full text Add to dashboard Cite

With the promotion and financial subsidies of the new energy vehicle (NEV), the NEV industry of China has developed rapidly in recent years. However, compared with traditional fuel vehicles, the technological maturity of the NEV is still insufficient, and there are still many problems that need to be solved in the R&D and operation stages. Among them, energy consumption and driving range are particularly concerning, and are closely related to the driving style of the driver. Therefore, the accurate identification of the driving style can provide support for the research of energy consumption. Based on the NEV high-frequency big data collected by the vehicle-mounted terminal, we extract the feature parameter set that can reflect the precise spatiotemporal changes in driving behavior, use the principal component analysis method (PCA) to optimize the feature parameter set, realize the automatic driving style classification using a K-means algorithm, and build a driving style recognition model through a neural network algorithm. The result of this paper shows that the model can automatically classify driving styles based on the actual driving data of NEV users, and that the recognition accuracy can reach 96.8%. The research on driving style recognition in this paper has a certain reference value for the development and upgrade of NEV products and the improvement of safety.

show abstract

Driving Style Identification Algorithm with Real-World Data Based on Statistical Approach

Cited by 11 publications

References 15 publications

Vehicle and Driver Monitoring System Using On-Board and Remote Sensors

Vehicle and Driver Monitoring System Using On-Board and Remote Sensors

CarEs: An Emotional Model of a Car With the Stress Factor

Driving Style Recognition Model Based on NEV High-Frequency Big Data and Joint Distribution Feature Parameters

Contact Info

Product

Resources

About