Driver curve speed model and its application to ACC speed control in curved roads

Zhang, D.; Xiao, Qing; Li, K.

doi:10.1007/s12239-013-0027-x

Cited by 21 publications

(13 citation statements)

References 9 publications

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“…The integration of a curve speed control function with an ACC system is presented in [28]. The system first fits a twoparameters function onto the entry and exit speed profiles observed for human drivers in manual operation.…”

Section: B Related Workmentioning

confidence: 99%

On Curve Negotiation: From Driver Support to Automation

Bosetti

Lio

Saroldi

2015

IEEE Trans. Intell. Transport. Syst.

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This paper describes a curve negotiation "behavior" that can be used-within subsumption architectures-to produce artificial agents with the ability to negotiate curves in a humanlike way. This may be used to implement functions spanning different levels of automation, from assistance (curve warning) to automated (curve speed control). This paper gives the following: 1) a summary of related works and of the subsumption architecture conceptual framework; 2) a detailed description of the function within this framework; 3) experimental data for validation and tuning derived from user tests; 4) guidelines on integration of the function within advanced driver assistance systems with different automation levels, with examples; and 5) a comparison with experimental data of the human curve speed choice models in the state of the art.Index Terms-Advanced driver assistance systems (ADAS), anticipatory longitudinal control, co-driver, curve speed, driver modeling, optimal control (OC).

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Section: B Related Workmentioning

confidence: 99%

On Curve Negotiation: From Driver Support to Automation

Bosetti

Lio

Saroldi

2015

IEEE Trans. Intell. Transport. Syst.

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“…Hull et al [16] collected the vehicle data with his CarTel system. There has also been expansive work into adaptive cruise control, which uses prediction algorithms to adapt to the road based on curve patterns [31]. Moreover, Rass et al [24] formulated a system to provide feedback on the driving habits.…”

Section: Related Workmentioning

confidence: 99%

“…The connectivity is enabled through the use of wireless communication over a dedicated spectrum to create local ad hoc networks of vehicles that are then able to communicate with other vehicles in its neighborhood, as well as with traffic infrastructure. The communication network provides opportunity for mobile devices or inertial sensors mounted in the vehicles to collect data regarding vehicle travel, driver behavior, and location data [19], [31]. This data can be processed for implementing automatic collision avoidance, optimizing traffic in real time, and planning for infrastructure needs.…”

Section: Introductionmentioning

confidence: 99%

Privacy preserving driving style recognition

Rizzo

Sprissler

Yuan

et al. 2015

2015 International Conference on Connected Vehicles and Expo (ICCVE)

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In order to better manage the premiums and encourage safe driving, many commercial insurance companies (e.g., Geico, Progressive) are providing options for their customers to install sensors on their vehicles which collect individual vehicle's traveling data. The driver's insurance is linked to his/her driving behavior. At the other end, through analyzing the historical traveling data from a large number of vehicles, the insurance company could build a classifier to predict a new driver's driving style: aggressive or defensive. However, collection of such vehicle traveling data explicitly breaches the drivers' personal privacy. To tackle such privacy concerns, this paper presents a privacypreserving driving style recognition technique to securely predict aggressive and defensive drivers for the insurance company without compromising the privacy of all the participating parties. The insurance company cannot learn any private information from the vehicles, and vice-versa. Finally, the effectiveness and efficiency of the privacy-preserving driving style recognition technique are validated with experimental results.

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“…In the context of road design and road safety, many studies model the speed at which a driver can safely negotiate a turn based on the geometrical characteristics of that turn. While this is a useful limit for assistance systems to take into account as an upper boundary, it does not provide information about a driver's desired speed and is consequently not directly usable in Advanced Driver Assistance Systems (ADAS) applications [7]. We wish to model the manner in which drivers themselves choose their speed, depending on their preference of driving style, risk, or safety margins.…”

Section: Introductionmentioning

confidence: 99%

A Perceptually Inspired Driver Model for Speed Control in Curves

Gruppelaar

Paassen

Mulder

et al. 2018

2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Understanding speed control in driving is important for analysis of road geometry and for the development of driver support assistance devices. Current models for speed selection are primarily based on the relation between road geometry and observed speeds. This study proposes a more detailed model that relates individual speed control to accelerator and brake pedal, based on perception of the visual scene as captured by the Extended Tangent Point (ETP). We investigated the potential of the the Time to ETP (TETP) as input for accelerator and brake pedal control. Based on observations from driving studies, we propose a model, and tuning rules to adjust the model parameters to observed behavior. A simulator experiment showed that, after individualization of the thresholds using a binary classification method, the model is capable of accurately capturing individual speed adaptation of 15 drivers on single lane roads with multiple curves.

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Driver curve speed model and its application to ACC speed control in curved roads

Cited by 21 publications

References 9 publications

On Curve Negotiation: From Driver Support to Automation

On Curve Negotiation: From Driver Support to Automation

Privacy preserving driving style recognition

A Perceptually Inspired Driver Model for Speed Control in Curves

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