Driver behaviour profiles for road safety analysis

Ellison, Adrian B.; Greaves, Stephen; Bliemer, Michiel C.J.

doi:10.1016/j.aap.2015.01.009

Cited by 112 publications

(51 citation statements)

References 28 publications

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“…Energy consumption of an EV depends on the characteristics of the vehicle and its drivetrain, the drive cycle (the speed profile driven) and auxiliary consumption. In real-world driving, this speed profile-and therefore energy consumption-is extremely variable and dependent on both road characteristics [9,10], such as road type and altitude profile, and driving style [11,12]. Additionally, the speed profile is affected by a number of external influences, such as traffic [13], weather [14] and driver mood, which either influence the behavior of or impose a behavior on the driver and trigger the use of auxiliaries.…”

Section: Introduction and State-of-the-artmentioning

confidence: 99%

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A Data-Driven Method for Energy Consumption Prediction and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions

et al. 2017

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Limited driving range remains one of the barriers for widespread adoption of electric vehicles (EVs). To address the problem of range anxiety, this paper presents an energy consumption prediction method for EVs, designed for energy-efficient routing. This data-driven methodology combines real-world measured driving data with geographical and weather data to predict the consumption over any given road in a road network. The driving data are linked to the road network using geographic information system software that allows to separate trips into segments with similar road characteristics. The energy consumption over road segments is estimated using a multiple linear regression (MLR) model that links the energy consumption with microscopic driving parameters (such as speed and acceleration) and external parameters (such as temperature). A neural network (NN) is used to predict the unknown microscopic driving parameters over a segment prior to departure, given the road segment characteristics and weather conditions. The complete proposed model predicts the energy consumption with a mean absolute error (MAE) of 12-14% of the average trip consumption, of which 7-9% is caused by the energy consumption estimation of the MLR model. This method allows for prediction of energy consumption over any route in the road network prior to departure, and enables cost-optimization algorithms to calculate energy efficient routes. The data-driven approach has the advantage that the model can easily be updated over time with changing conditions.

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Section: Introduction and State-of-the-artmentioning

confidence: 99%

“…Traffic density can influence the driving behavior by imposing a de facto maximum speed or a higher frequency of stops and accelerations. Weather, in the form of temperature, rain and daylight might influence the driving behavior towards a more cautious style to lower the risk for accidents [12].…”

Section: Introduction and State-of-the-artmentioning

confidence: 99%

A Data-Driven Method for Energy Consumption Prediction and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions

et al. 2017

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“…Emotional state, physical state, risk propensity, skill level, and external conditions contribute to driving impairment and can be objectively monitored from telematics data. In fact, over 90% of vehicle accidents can be attributed to human error (Ellison, Greaves, & Bliemer, ). We extend such approaches to autonomous systems to monitor technological errors.…”

Section: Emerging Technology Risksmentioning

confidence: 99%

Semiautonomous Vehicle Risk Analysis: A Telematics‐Based Anomaly Detection Approach

2018

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The transition to semiautonomous driving is set to considerably reduce road accident rates as human error is progressively removed from the driving task. Concurrently, autonomous capabilities will transform the transportation risk landscape and significantly disrupt the insurance industry. Semiautonomous vehicle (SAV) risks will begin to alternate between human error and technological susceptibilities. The evolving risk landscape will force a departure from traditional risk assessment approaches that rely on historical data to quantify insurable risks. This article investigates the risk structure of SAVs and employs a telematics-based anomaly detection model to assess split risk profiles. An unsupervised multivariate Gaussian (MVG) based anomaly detection method is used to identify abnormal driving patterns based on accelerometer and GPS sensors of manually driven vehicles. Parameters are inferred for vehicles equipped with semiautonomous capabilities and the resulting split risk profile is determined. The MVG approach allows for the quantification of vehicle risks by the relative frequency and severity of observed anomalies and a location-based risk analysis is performed for a more comprehensive assessment. This approach contributes to the challenge of quantifying SAV risks and the methods employed here can be applied to evolving data sources pertinent to SAVs. Utilizing the vast amounts of sensor-generated data will enable insurers to proactively reassess the collective performances of both the artificial driving agent and human driver.

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“…The objective here was to identify behaviours and their relationship to crash risk. These (individual) behavioural measures correctly predicted 68% of crash-involved drivers (26 drivers) and 87% of non-crash-involved drivers (141 drivers) [7].…”

Section: Introductionmentioning

confidence: 99%

The Research on Ageing of Glycol-Based Brake Fluids of Vehicles in Operation

Caban¹,

Dróździel²,

Vrábel³

et al. 2016

Adv. Sci. Technol. Res. J.

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The road safety depends on many factors, including the efficiency of the technical system and the disposal of the driver of the vehicle. As wear of most of the vehicle assemblies threatened only with a breakdown, excessive wear of the braking system components may pose a risk of a traffic accident. The article presents a study on the quality of the brake fluid conductivity as a function of temperature and water content conducted for a selected group of vehicles. This group of vehicles was characterized by different production date and had a different of vehicle mileage. We presented the methodology of statistical analysis of the quality of brake fluid tests in selected 38 passenger vehicles in operation.

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Driver behaviour profiles for road safety analysis

Cited by 112 publications

References 28 publications

A Data-Driven Method for Energy Consumption Prediction and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions

A Data-Driven Method for Energy Consumption Prediction and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions

Semiautonomous Vehicle Risk Analysis: A Telematics‐Based Anomaly Detection Approach

The Research on Ageing of Glycol-Based Brake Fluids of Vehicles in Operation

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