Increasing complexity of driving situations and its impact on an ADAS for anticipatory assistance for the reduction of fuel consumption

Rommerskirchen, Christoph; Helmbrecht, Magnus; Bengler, Klaus

doi:10.1109/ivs.2013.6629528

Cited by 11 publications

(9 citation statements)

References 3 publications

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“…Here, potential savings of up to 13% were found, at the cost of less than 3% increase in total travel time [1]. Research investigating the consistency of this effect across traffic situations with varying complexity also used an interface to aid the driver in the decision of when to brake or coast, and found a reduction in overall fuel consumption of about 10% [21]. While visual fixations on the interface were dependent on the complexity of the given traffic situation, reductions in fuel consumption stayed constant.…”

Section: The Current State Of Anticipation In Driving Research Benefimentioning

confidence: 94%

A Model of Anticipation in Driving

Stahl

Donmez

Jamieson

2014

Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

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The ability to anticipate future events in the traffic environment is an important competence in driving. This paper extends our prior work: 1) to show potential benefits resulting from anticipatory competence in driving, and 2) to collate characteristics of anticipatory competence from a theoretical point of view. The reviewed literature is foundational to our understanding of anticipation as a highlevel cognitive competence, allowing for the prediction of future traffic situations on a tactical level. We conceptualize anticipation as relying on the identification of stereotypical traffic situations based on indicative cues, and stress that the impacts of this competence are dependent on the driver's individual goals. Thus, anticipation enables a number of potential benefits, such as safety and fuel-efficiency, but the realization of these potential benefits depends on the goals of the driver. Further, we argue that the superior anticipatory competence of experienced drivers observed in an earlier simulator study can be explained via their heightened ability both to identify indicative cues, and interpret those cues relative to similar, memorized situations. We then capture anticipatory driving in a model inspired by the classical theory of information processing to describe the various steps necessary to process indicative cues from the environment, anticipate a future traffic situation, and take appropriate action or achieve a state of cognitive readiness.

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Section: The Current State Of Anticipation In Driving Research Benefimentioning

confidence: 94%

A Model of Anticipation in Driving

Stahl

Donmez

Jamieson

2014

Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

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“…The solutions, which provide information in advance about the traffic state or road signs, are known as Advanced Driver Assistance System (ADAS). In [35], the authors analyzed different rural and urban deceleration scenarios such as "approaching a speed limit" or "left turn at a green traffic light" in an urban scenario. The overall fuel consumption was reduced by about 10%.…”

Section: Ecodriving Assistantmentioning

confidence: 99%

WATI: Warning of Traffic Incidents for Fuel Saving

Magaña

Muñoz-Organero

2016

Mobile Information Systems

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Traffic incidents (heavy traffic, adverse weather conditions, and traffic accidents) cause an increase in the frequency and intensity of the acceleration and deceleration. The result is a very significant increase in fuel consumption. In this paper, we propose a solution to reduce the impact of such events on energy consumption. The solution detects the traffic incidents based on measured telemetry data from vehicles and the different driver profiles. The proposal takes into account the rolling resistance coefficient, the road slope angle, and the vehicles speeds, from vehicles which are on the scene of the traffic incident, in order to estimate the optimal deceleration profile. Adapted advice and feedback are provided to the drivers in order to appropriately and timely release the accelerator pedal. The expert system is implemented on Android mobile devices and has been validated using a dataset of 150 tests using 15 different drivers. The main contribution of this paper is the proposal of a system to detect traffic incidents and provide an optimal deceleration pattern for the driver to follow without requiring sensors on the road. The results show an improvement on the fuel consumption of up to 13.47%.

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“…Maximum deceleration can be found by Equation (3) in which μ is the friction coefficient, g is the acceleration of gravity, and θ is the gradient. Gradient is calculated by using DGPS data as shown in Equation (2). Time until stopping can be calculated as shown in Equation (4).…”

Section: Figure 3 Effect According To Gradientmentioning

confidence: 99%

“…Thus, acci-dents that occur by late braking timing owing to carelessness such as distraction and dozing off while driving make up a large part of the total number of accidents. As interest in car safety has increased, research on advanced driver assistant systems (ADAS) is being actively conducted in the automobile market, to prevent accidents due to the carelessness of drivers [2]. Among several functions in the ADAS, the AEB system analyzes the collision risk with the vehicle ahead and prevents or avoids accidents by automatic braking in dangerous situations [3].…”

Section: Introductionmentioning

confidence: 99%