Optimisation of energy efficiency based on average driving behaviour and driver's preferences for automated driving

Themann, Philipp; Bock, Julian; Eckstein, Lutz

doi:10.1049/iet-its.2013.0121

Cited by 25 publications

(18 citation statements)

References 15 publications

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“…The first category includes studies seeking to optimise fuel consumption for simple scenarios, where there are no additional complexities caused by interactions between vehicles. In this case information about roadway topography or the position of traffic signals is used in order to formulate an optimisation problem and obtain the optimal velocity profile (Wu, et al, 2011;Themann, et al, 2015;Markschläger, et al, 2012;Hellström, et al, 2010;Kohut, et al, 2009). The second category, on the other hand, consists of studies targeting driving conditions, where interaction between vehicles is the defining factor in driving behaviour.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For this purpose the objective function was discretised and the resulting optimisation problem was then solved using the Lagrange Multiplier Method (LMM). Themann et al (2015) proposed a control model for Adaptive Cruise Control systems (ACC) that relied on the optimisation of the velocity profile with respect to fuel consumption. This study used Dijkstra's algorithm to find the optimal velocity profile for known road topography.…”

Section: Introductionmentioning

confidence: 99%

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A framework for user- and system-oriented optimisation of fuel efficiency and traffic flow in Adaptive Cruise Control

Mamouei

Kaparias

Halikias

2018

Transportation Research Part C: Emerging Technologies

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Fully automated vehicles are expected to have a significant share of the road network traffic in the near future. Several commercial vehicles with full-range Adaptive Cruise Control (ACC) systems or semi-autonomous functionalities are already available on the market. Many research studies aim at leveraging the potential of automated driving in order to improve the fuel efficiency of vehicles. However, in the vast majority of those, fuel efficiency is isolated to the driving dynamics between a single follower-leader pair, hence overlooking the complex nature of traffic. Consequently fuel efficiency and the efficient use of the roadway capacity are framed as conflicting objectives, leading to fuel-economy control models that adopt highly conservative driving styles. This formulation of the problem could be seen as a user-optimal approach, where in spite of delivering savings for individual vehicles, there is the side-effect of the deterioration of traffic flow. An important point that is overlooked is that the inefficient use of roadway capacity gives rise to congested traffic and traffic breakdowns, which in return increases energy costs within the system. The optimisation methods used in these studies entail high computational costs and, therefore, impose a strict constraint on the scope of problem. In this study, the use of car-following models and the limitation of the search space of optimal strategies to the parameter space of these is proposed. The proposed framework enables performing much more comprehensive optimisations and conducting more extensive tests on the collective impacts of fuel-economy driving strategies. The results show that, as conjectured, a "short-sighted" user-optimal approach is unable to deliver overall fuel efficiency. Conversely, a system-optimal formulation for fuel efficient driving is presented, and it is shown that the objectives of fuel efficiency and traffic flow are in fact not only non-conflicting, but also that they could be viewed as one when the global benefits to the network are considered.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A framework for user- and system-oriented optimisation of fuel efficiency and traffic flow in Adaptive Cruise Control

Mamouei

Kaparias

Halikias

2018

Transportation Research Part C: Emerging Technologies

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“…The second contribution is the development of an algorithm to automatically create a driving corridor from a speed reference and statistics from real truck operation. This paper uses statistics from live heavy-duty truck operation and not from experiments as in Themann and Eckstein (2012) and Themann et al (2015). In addition, all types of decelerations are considered, i.e., from any start speed to any end speed.…”

Section: Pos [M]mentioning

confidence: 99%

“…From the resulting decelerations, the mean value and standard deviation are calculated and used in order to adapt the driving strategy to driver preferences. In Themann et al (2015), these statistics are used in order to create a driving corridor which consists of the maximum and minimum allowed velocities.…”

Section: Introductionmentioning

confidence: 99%

Optimal Speed Trajectories Under Variations in the Driving Corridor

2017

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The optimal speed trajectory for a heavy-duty truck is calculated using the Pontryagin's maximum principle. The truck motion depends on controllable tractive and braking forces and external forces such as air and rolling resistance and road slope. The velocity of the vehicle is restricted to be within a driving corridor which consists of an upper and a lower boundary. Simulations are performed on data from a test cycle commonly used for testing distribution driving. The data include road slope and a speed reference, from which the driving corridor is created automatically. The simulations include a sensitivity analysis on how changes in the parameters for the driving corridor influence the energy consumption and trip time.For the widest driving corridor tested, 15.8 % energy was saved compared to the most narrow corridor without increasing the trip time. Most energy was saved by reducing the losses due to braking and small amounts of energy were saved by reducing the losses due to air resistance. Finally, optimal trajectories with the same trip time derived from different settings on the driving corridor are compared in order to analyse energy efficient driving patterns.

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“…Only then an anomalous behavior can be accurately recognized. In an earlier work, Themann et al [10] noted that there is a strong need to identify driver's unique preferences and incorporate such models in adaptive cruise control systems (anticipating driving style) so that a largely automated driving can fulfill its promise of improving fuel efficiency.…”

Section: Introductionmentioning

confidence: 99%

Investigations on Driver Unique Identification from Smartphone’s GPS Data Alone

Chowdhury

Chakravarty

Ghose

et al. 2018

Journal of Advanced Transportation

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Driver identification is an emerging area of interest in vehicle telematics, automobile control, and insurance. Recent body of works indicates that it may be possible to uniquely identify a driver using multiple dedicated sensors. In this paper, we present an approach for driver identification using smartphone GPS data alone. For our experiments, we collected data from 38 drivers for two months. We quantified the driver's natural style by extracting a set of 137 statistical features from data generated for each completed trip. The analysis shows that, for the "driver identification" problem, an average accuracy of 82.3% is achieved for driver groups of 4-5 drivers. This is comparable to the state of the arts where mostly a multisensor approach has been taken. Further, it is shown that certain behavioral attributes like high driving skill impact identification accuracy. We observe that Random Forest classifier offers the best results. These results have great implications for various stakeholders since the proposed method can identify a driver based on his/her naturalistic driving style which is quantified in terms of statistical parameters extracted from only GPS data.

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Optimisation of energy efficiency based on average driving behaviour and driver's preferences for automated driving

Cited by 25 publications

References 15 publications

A framework for user- and system-oriented optimisation of fuel efficiency and traffic flow in Adaptive Cruise Control

A framework for user- and system-oriented optimisation of fuel efficiency and traffic flow in Adaptive Cruise Control

Optimal Speed Trajectories Under Variations in the Driving Corridor

Investigations on Driver Unique Identification from Smartphone’s GPS Data Alone

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