Driving style recognition for intelligent vehicle control and advanced driver assistance: a survey

Driver driving style plays an important role in vehicle energy management as well as driving safety. Furthermore, it is key for advance driver assistance systems development, toward increasing levels of vehicle automation. This fact has motivated numerous research and development efforts on driving style identification and classification. This paper provides a survey on driving style characterization and recognition revising a variety of algorithms, with particular emphasis on machine learning approaches based on current and future trends. Applications of driving style recognition to intelligent vehicle controls are also briefly discussed, including experts' predictions of the future development

A BAYESIAN NETWORK APPROACH TO BATTERY AGING IN ELECTRIC VEHICLE TRANSPORTATION AND GRID INTEGRATION

Nowadays, batteries in electric vehicles (EVs) are facing a variety of tasks in their connection to the power grid in addition to the main task, driving. All of these tasks play a very significant role in the battery aging, but they are highly variable due to the change in the driver behavior, grid connection availability and weather conditions. The effect of these external factors in the battery degradation have been studied in literature by mostly deterministic and some stochastic approaches, but limited to specific cases. In this dissertation, first, a large-scale deterministic approach is implemented to evaluate the effect of variations in the EV battery daily tasks. To do so, a software tool named REV-Cycle is developed to simulate the EV powertrain and studied the effect of driving behavior, recharging facilities and timings, grid services and temperature/weather change effects, one by one. However, there are two main problems observed in the deterministic aging evaluation: First, the battery capacity fade factors such as temperature, cycling current, state of charge (SOC) … are dependent to the external variables such as location, vehicle owner’s behavior and availability of the grid connection. Therefore, it is not possible to accurately evaluate the battery degradation with a deterministic model, while its inputs are stochastic. Second, the battery aging factors’ dependency is hierarchical and it is not easy to follow and implement this hierarchy with deterministic models. Therefore, using a hierarchical probabilistic framework is proposed that can better represent the problem and realized that the Bayesian statistics with Markov Chain Monte Carlo (MCMC) can provide the problem solving structure needed for this purpose. A comprehensive hierarchical probabilistic model of the battery capacity fade is proposed using Hierarchical Bayesian Networks (HBN). The model considers all uncertainties of the process including vehicle acceleration and velocity, grid connection for charging and utility services, temperatures and all unseen intermediate variables such as battery power, auxiliary power, efficiencies, etc. and estimates the capacity fade as a probability distribution. Metropolis-Hastings MCMC algorithm is applied to generate the posterior distributions. This modeling approach shows promising result in different case studies and provides more informative evaluation of the battery capacity fade

Holistic Vehicle Instrumentation for Assessing Driver Driving Styles

Nowadays, autonomous vehicles are increasing, and the driving scenario that includes both autonomous and human-driven vehicles is a fact. Knowing the driving styles of drivers in the process of automating vehicles is interest in order to make driving as natural as possible. To this end, this article presents a first approach to the design of a controller for the braking system capable of imitating the different manoeuvres that any driver performs while driving. With this aim, different experimental tests have been carried out with a vehicle instrumented with sensors capable of providing real-time information related to the braking system. The experimental tests consist of reproducing a series of braking manoeuvres at different speeds on a flat floor track following a straight path. The tests distinguish between three types of braking manoeuvre: maintained, progressive and emergency braking, which cover all the driving circumstances in which the braking system may intervene. This article presents an innovative approach to characterise braking types thanks to the methodology of analysing the data obtained by sensors during experimental tests. The characterisation of braking types makes it possible to dynamically classify three driving styles: cautious, normal and aggressive. The proposed classifications allow it possible to identify the driving styles on the basis of the pressure in the hydraulic brake circuit, the force exerted by the driver on the brake pedal, the longitudinal deceleration and the braking power, knowing in all cases the speed of the vehicle. The experiments are limited by the fact that there are no other vehicles, obstacles, etc. in the vehicle's environment, but in this article the focus is exclusively on characterising a driver with methods that use the vehicle's dynamic responses measured by on-board sensors. The results of this study can be used to define the driving style of an autonomous vehicle

Eco-driving technology for sustainable road transport: A review

© 2018 Elsevier Ltd Road transport consumes significant quantities of fossil fuel and accounts for a significant proportion of CO2 and pollutant emissions worldwide. The driver is a major and often overlooked factor that determines vehicle performance. Eco-driving is a relatively low-cost and immediate measure to reduce fuel consumption and emissions significantly. This paper reviews the major factors, research methods and implementation of eco-driving technology. The major factors of eco-driving are acceleration/deceleration, driving speed, route choice and idling. Eco-driving training programs and in-vehicle feedback devices are commonly used to implement eco-driving skills. After training or using in-vehicle devices, immediate and significant reductions in fuel consumption and CO2 emissions have been observed with slightly increased travel time. However, the impacts of both methods attenuate over time due to the ingrained driving habits developed over the years. These findings imply the necessity of developing quantitative eco-driving patterns that could be integrated into vehicle hardware so as to generate more constant and uniform improvements, as well as developing more effective and lasting training programs and in-vehicle devices. Current eco-driving studies mainly focus on the fuel savings and CO2 reduction of individual vehicles, but ignore the pollutant emissions and the impacts at network levels. Finally, the challenges and future research directions of eco-driving technology are elaborated

Quantitative Performance Assessment of LiDAR-based Vehicle Contour Estimation Algorithms for Integrated Vehicle Safety Applications

Many nations and organizations are committing to achieving the goal of `Vision Zero\u27 and eliminate road traffic related deaths around the world. Industry continues to develop integrated safety systems to make vehicles safer, smarter and more capable in safety critical scenarios. Passive safety systems are now focusing on pre-crash deployment of restraint systems to better protect vehicle passengers. Current commonly used bounding box methods for shape estimation of crash partners lack the fidelity required for edge case collision detection and advanced crash modeling. This research presents a novel algorithm for robust and accurate contour estimation of opposing vehicles. The presented method is evaluated via a developed framework for key performance metrics and compared to alternative algorithms found in literature

The Psychology of Vehicle Performance: Implications for the Uptake of Electric Vehicles

Road transport accounts for around 16% of global CO2 emissions, and electric vehicles (EVs) represent a potential mitigation route. High performance might offset the disadvantages of higher cost and short range that make their uptake problematic. This research investigated how consumer drivers construe, perceive and value vehicle performance. Research with UK drivers, using the repertory grid method, found that drivers construe vehicle performance as having two independent dimensions, dynamic and cruising performance. A new inter-goal dynamics and feedback control model of driving behaviour was developed to account for differences in the opportunities afforded to perceive vehicle performance in naturalistic driving. This was embedded in a Bayesian model for perception of available vehicle performance. Driving simulation and test track experiments with UK drivers found that: driving behaviour was strongly affected by goal activation; drivers could perceive performance differences in naturalistic driving, but only if they were large; the lowest perceptual difference threshold, for mid-range available vehicle acceleration, was 7.7%; smaller differences could affect driving behaviour (overtaking) through a process of implicit learning. The symbolic value of products is conferred by their symbolic meanings. Two new methods were developed to quantify symbolic meanings, grounded in costly signalling theory, which represents them in terms of personality traits of a typical user. The symbolic meanings of car types, performance attributes and driving styles were all measured. In a randomised controlled trial, UK consumer drivers rated an EV better on dynamic and cruising performance than a conventional ICE control, but this benefit was insufficient to outweigh the disadvantages. The symbolic meaning of an EV was found to be consistent with cruising performance, but inconsistent with dynamic performance. Extended-range EVs would have the dynamic and cruising performance benefits of EVs without the range disadvantages, and may be a desirable option for many once costs reduce