Fusion of Driver Behaviour Analysis and Situation Assessment for Probabilistic Driving Manoeuvre Prediction

Leonhardt, Veit; Pech, Timo; Wanielik, Gerd

doi:10.1007/978-3-658-15418-9_12

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…However, discomfort associated with gaps that are too close or potential rear-end collisions could be detected involving body motion. A potential approach for data fusion algorithms could be the inclusion of environment sensor information such as time headway (Leonhardt et al, 2017 ) and to consider the pushback motion pattern only in these situations.…”

Section: Discussionmentioning

confidence: 99%

“…The nodes of such a network allow for integration of environment information (such as presence of a vehicle driving ahead) as well as for “inverting” the algorithm, once discomfort was detected, in order to return to the baseline. This method has already been applied by the Communication Engineering Department at Chemnitz University of Technology for real-time prediction of lane change maneuvers, combining parameters from the driver, the vehicle and the environment (Leonhardt et al, 2017 ).…”

Section: Discussionmentioning

confidence: 99%

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Using Smartbands, Pupillometry and Body Motion to Detect Discomfort in Automated Driving

Beggiato

Hartwich

Krems

2018

Front. Hum. Neurosci.

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As technological advances lead to rapid progress in driving automation, human-machine interaction (HMI) issues such as comfort in automated driving gain increasing attention. The research project KomfoPilot at Chemnitz University of Technology aims to assess discomfort in automated driving using physiological parameters from commercially available smartbands, pupillometry and body motion. Detected discomfort should subsequently be used to adapt driving parameters as well as information presentation and prevent potentially safety-critical take-over situations. In an empirical driving simulator study, 40 participants from 25 years to 84 years old experienced two highly automated drives with three potentially critical and discomfort-inducing approaching situations in each trip. The ego car drove in a highly automated mode at 100 km/h and approached a truck driving ahead with a constant speed of 80 km/h. Automated braking started very late at a distance of 9 m, reaching a minimum of 4.2 m. Perceived discomfort was assessed continuously using a handset control. Physiological parameters were measured by the smartband Microsoft Band 2 and included heart rate (HR), heart rate variability (HRV) and skin conductance level (SCL). Eye tracking glasses recorded pupil diameter and eye blink frequency; body motion was captured by a motion tracking system and a seat pressure mat. Trends of all parameters were analyzed 10 s before, during and 10 s after reported discomfort to check for overall parameter relevance, direction and strength of effects; timings of increase/decrease; variability as well as filtering, standardization and artifact removal strategies to increase the signal-to-noise ratio. Results showed a reduced eye blink rate during discomfort as well as pupil dilation, also after correcting for ambient light influence. Contrary to expectations, HR decreased significantly during discomfort periods, whereas HRV diminished as expected. No effects could be observed for SCL. Body motion showed the expected pushback movement during the close approach situation. Overall, besides SCL, all other parameters showed changes associated with discomfort indicated by the handset control. The results serve as a basis for designing and configuring a real-time discomfort detection algorithm that will be implemented in the driving simulator and validated in subsequent studies.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Using Smartbands, Pupillometry and Body Motion to Detect Discomfort in Automated Driving

Beggiato

Hartwich

Krems

2018

Front. Hum. Neurosci.

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“…Recently, new approaches try to incorporate additional information about the situational context and especially of the traffic situation to allow an actual prediction of maneuver intentions. [18] presented a model for the recognition and prediction of lane changes based on both driver-based input and the traffic situation. To reduce complexity, they condensed the traffic situations into discrete levels of occupancy for each lane.…”

Section: Investigating Initial Driver Intention On Overtaking On Ruramentioning

confidence: 99%

Investigating Initial Driver Intention on Overtaking on Rural Roads

Yan

Eilers²,

Weber

et al. 2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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Driver intention recognition is essential to the development of advanced driver assistance systems providing real-time support. Current approaches for the recognition of overtaking intentions focus on drivers' observable behaviors, neglecting the fact that the intention to overtake a slower lead car emerges earlier than the resulting behavior. This paper aims to distinguish the "intention emerging process", when drivers form the initial intention to overtake, from the "action executing process", when drivers execute the overtaking maneuver. A driving simulator study has been conducted to investigate the influence of the lead vehicle type and lead vehicle speed on initiating driver' intention on overtaking on rural roads, and the effect of the complexity of the oncoming traffic on executing overtaking. The results show that the initial driver intention to overtake appears much earlier than the execution of the overtaking maneuver. The lead vehicle speed has a significant influence on initial driver intention in the "intention emerging process", while time to overtake increases with the number of the oncoming vehicles in the "action execution process". These results can contribute to the development of models for driver intention recognition by extending the prediction horizon from the recognition to a prediction of driving maneuvers.

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Situation Assessment-Augmented Interactive Kalman Filter for Multi-Vehicle Tracking

Khalkhali

Vahedian

Yazdi

2022

IEEE Trans. Intell. Transport. Syst.

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Fusion of Driver Behaviour Analysis and Situation Assessment for Probabilistic Driving Manoeuvre Prediction

Cited by 4 publications

References 6 publications

Using Smartbands, Pupillometry and Body Motion to Detect Discomfort in Automated Driving

Using Smartbands, Pupillometry and Body Motion to Detect Discomfort in Automated Driving

Investigating Initial Driver Intention on Overtaking on Rural Roads

Situation Assessment-Augmented Interactive Kalman Filter for Multi-Vehicle Tracking

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