The time course of a lane change: Driver control and eye-movement behavior

Salvucci, Dario D.; Liu, Aizhong

doi:10.1016/s1369-8478(02)00011-6

Cited by 244 publications

(119 citation statements)

References 7 publications

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“…Other studies more recently have included eye gaze measurements as a part of laboratory tests of driver fatigue [19], [20] or of simulated lane change events [21], [22]. Simulators though do not capture all the dynamics and variability of real-world environments [4].…”

Section: A Related Research In Lane Change Behavior Analysismentioning

confidence: 99%

“…Based on the prior research and the optimal results in [1], we decided to obtain results for two decision times of 2 and 3 s prior to the lane change. By detecting a lane change this far in advance, an ADAS would be able to warn the driver in time for the driver to be able to safely react [1], [21], [37]. In each case, data in a window of 1 s prior to that decision time were used to make the decision.…”

Section: Experiments and Analysismentioning

confidence: 99%

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On the Roles of Eye Gaze and Head Dynamics in Predicting Driver's Intent to Change Lanes

Doshi

Trivedi

2009

IEEE Trans. Intell. Transport. Syst.

243

124

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Abstract-Driver behavioral cues may present a rich source of information and feedback for future intelligent advanced driver-assistance systems (ADASs). With the design of a simple and robust ADAS in mind, we are interested in determining the most important driver cues for distinguishing driver intent. Eye gaze may provide a more accurate proxy than head movement for determining driver attention, whereas the measurement of head motion is less cumbersome and more reliable in harsh driving conditions. We use a lane-change intent-prediction system Index Terms-Driver-assistance systems, driver behavior, driver intent inference, intelligent vehicles, machine vision, sparse Bayesian learning.

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Section: A Related Research In Lane Change Behavior Analysismentioning

confidence: 99%

Section: Experiments and Analysismentioning

confidence: 99%

On the Roles of Eye Gaze and Head Dynamics in Predicting Driver's Intent to Change Lanes

Doshi

Trivedi

2009

IEEE Trans. Intell. Transport. Syst.

243

124

View full text Add to dashboard Cite

show abstract

“…We can see the vehicle follows the pursuit point gracefully in an "s" curve from the centerline of the current lane to the centerline of the target changing lane. This is consistent with the result reported by Salvucci [16] from driving simulator data generated with real drivers. …”

Section: The Action Of Lane Changingsupporting

confidence: 93%

“…A substantial body of literature [1,11,16] on transportation research has examined driving behaviors of real drivers. Some of the concepts we use in our work such as discretionary lane changing and mandatory lane changing are motivated by this body of research.…”

Section: Ieee Virtual Reality 2005mentioning

confidence: 99%

Steering Behaviors for Autonomous Vehicles in Virtual Environments

Wang

Kearney

Cremer

et al.

IEEE Virtual Reality Conference 2005 (VR'05)

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This paper presents steering behaviors that control autonomous vehicles populating roadways in virtual urban environments. Behavior programming is facilitated by a set of representations of the environment that use convenient frames of reference in natural coordinate systems. Roadway surfaces are modeled as three-dimensional ribbons that make the local orientation of the road explicit and allow relative distances on the road to be simply computed. Roads and intersections are connected to form a ribbon network. An egocentric representation called a path melds road and intersection segments into a single, continuous ribbon that captures the vehicle's shortterm plan of navigation. A topological structure called a route supports wayfinding. We describe how the interrelated ribbon, path, and route representations are used to build multi-component behaviors that plan routes and safely navigate through traffic filled road networks -tracking lanes, shifting lanes to avoid congestion, anticipating lane changes needed to make turns dictated by the route, negotiating intersections, and respecting the rules of the road.

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“…Depending on the situation, some sensors may bring more information than others (Salvucci & Liu, 2002). For example, to model a "Turn left," the steering wheel seems more important than the speed of the vehicle.…”

Section: Weight Semi Hidden Markov Modelmentioning

confidence: 99%

Weight Semi Hidden Markov Model and Driving Situation Classification for Driver Behavior Diagnostic

Dapzol¹

2007

Proceedings of the 4th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle

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Summary:In this study, we propose to use statistical modelling to analyze, model, and categorize driving activity. To achieve this objective, we develop a new statistical model by adding a weight feature to the classic Semi Hidden Markov Model (SHMM) framework. Then, to assess its capacity, we conduct an experiment that allows us to record 718 driving sequences categorized in 36 situations. We then used our modelling to identify the driver's aim and the driving situation he's in. Furthermore, we adapted the ascendant hierarchic classification technique to this modelling. It allows us to understand which situations are close and to define partitions of whole driving situations. Finally, on these sequences, our modelling choice allows us to predict the driver's situation with, on average, an 85% success rate. These results show the HMM effectiveness to manage temporal and multidimensional data by modelling predicting drivers' behavior.

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The time course of a lane change: Driver control and eye-movement behavior

Cited by 244 publications

References 7 publications

On the Roles of Eye Gaze and Head Dynamics in Predicting Driver's Intent to Change Lanes

On the Roles of Eye Gaze and Head Dynamics in Predicting Driver's Intent to Change Lanes

Steering Behaviors for Autonomous Vehicles in Virtual Environments

Weight Semi Hidden Markov Model and Driving Situation Classification for Driver Behavior Diagnostic

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