A collaborative approach for human-centered driver assistance systems

McCall, J.C.; Achler, O.; Trivedi, Mohan M.; Haué, Jean-Baptiste; Fastrez, Pierre; Förster, Deborah; Hollan, James D.; Boer, Erwin R.

doi:10.1109/itsc.2004.1398980

Cited by 20 publications

(15 citation statements)

References 2 publications

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“…One of the earliest works that demonstrates the use and analysis of data from multiple sensors in a vehicle is reported in [5]. Information from camera sensors sensing the vehicle surroundings is fused with vehicle dynamics data from CAN bus and GPS information to extract useful information about the driver behavior during lane changes.…”

Section: Related Workmentioning

confidence: 99%

“…Information from camera sensors sensing the vehicle surroundings is fused with vehicle dynamics data from CAN bus and GPS information to extract useful information about the driver behavior during lane changes. The need for such extensive data capturing, analysis and visualization tools that collaboratively function with each other is established in [5] for Intelligent Driver Safety Systems (IDSS).…”

Section: Related Workmentioning

confidence: 99%

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Towards automated drive analysis: A multimodal synergistic approach

Satzoda

Martin

et al. 2013

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

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Abstract-Naturalistic driving studies (NDS) capture huge amounts of drive data, that is analyzed for critical information about driver behavior, driving characteristics etc. Moreover, NDS involve data collected from a wide range of sensing technologies in cars and this makes the analysis of this data a challenging task. In this paper, we propose a multimodal synergistic approach for automated drive analysis process that can be employed in analyzing large amounts of drive data. The visual information from cameras, vehicle dynamics from CAN bus, vehicle global positioning coordinates from GPS and digital road map data, that are collected during the drive, are analyzed in a collaborative and complementary manner in the approach presented in this paper. It will be shown that the proposed synergistic drive analysis approach automatically determines a wide range of critical information about the drive in varying road conditions.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Towards automated drive analysis: A multimodal synergistic approach

Satzoda

Martin

et al. 2013

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

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“…In order to gain this insight, studies of driving and what information drivers rely on to make decisions and act are essential. These studies require capture of synchronized data of driving behavior and context [4]. Analysis tools are needed to extract the contextual cues and relevant signals from the numerous vehicle measurements of the vehicle and surround environment state.…”

Section: Driver Support Sensing and Systemsmentioning

confidence: 99%

Vehicle Iconic Surround Observer: Visualization platform for intelligent driver support applications

Morris

Trivedi

2010

2010 IEEE Intelligent Vehicles Symposium

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Abstract-With the introduction of intelligent driver support systems, vehicles have become more comfortable and safer. But, these systems require new sensors and the information they contain must be efficiently presented to the driver. The cognitive demands for interpreting these signals may prove to be a distraction with negative impact on driving performance. This work describes a unified visualization scheme, the Vehicle Iconic Surround Observer, capable of introducing new surround sensors into a common display environment which quickly conveys critical surround context with minimal driver interpretation.

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“…In [11], a Monte Carlo reasoning framework evaluates the probability of a future collision and use a Monte Carlo important sampling for the approximation of a collision integral. The looking-in and looking-out framework proposed by McCall et al is a system-oriented safer driving framework [12], which consists of driving ecology sensing, hierarchical context processing, and modeling of drivers, vehicles, and environment. They build the Human-Centered Intelligent Driving Support System to emphasize the role of driver.…”

Section: Previous Workmentioning

confidence: 99%

“…Moreover, the behavior of high-speed vehicles differs greatly from other robots. To obtain safe driving, a driver should be in the center of the safety analysis [12]; driver response delay, together with other factors, restricts the driving path of a vehicle. On the basis of these factors, we proposed an integrated interactive road safety analysis framework, where the system consists of the safety analysis framework consisting of the following modules: onboard sensor network, environment modeling and sensor fusion, vehicle ego-state and vehicle dynamics module, future situation assessment, decision-making agents, human-computer interface (HCI), and a preview-following model-based control module (see Fig.…”

Section: Interactive Safety Analysis Frameworkmentioning

confidence: 99%

Interactive Road Situation Analysis for Driver Assistance and Safety Warning Systems: Framework and Algorithms

Cheng

Zheng

Zhang

et al. 2007

IEEE Trans. Intell. Transport. Syst.

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Abstract-Road situation analysis in Interactive IntelligentDriver-Assistance and Safety Warning (I 2 DASW) systems involves estimation and prediction of the position and size of various on-road obstacles. Real-time processing, given incomplete and uncertain information, is a challenge for current object detection and tracking technologies. This paper proposed a development framework and novel algorithms for road situation analysis based on driving action behavior, where the safety situation is analyzed by simulating real driving action behaviors. First, we review recent development and trends in road situation analysis to provide perspective for the related research. Second, we introduce a road situation analysis framework, where onboard sensors provide information about drivers, traffic environment, and vehicles. Finally, on the basis of the previous frameworks, we proposed multiple-obstacle detection and tracking algorithms using multiple sensors including radar, lidar, and a camera, where a decentralized track-to-track fusion approach is introduced to fuse these sensors. In order to reduce the effect of obstacle shape and appearance, we cluster lidar data and then classify obstacles into two categories: static and moving objects. Future collisions are assessed by computation of local tracks of moving obstacles using extended Kalman filter, maximum likelihood estimation to fuse distributed local tracks into global tracks, and finally, computation of future collision distribution from the global tracks. Our experimental results show that our approach is efficient for road situation evaluation and prediction.

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A collaborative approach for human-centered driver assistance systems

Cited by 20 publications

References 2 publications

Towards automated drive analysis: A multimodal synergistic approach

Towards automated drive analysis: A multimodal synergistic approach

Vehicle Iconic Surround Observer: Visualization platform for intelligent driver support applications

Interactive Road Situation Analysis for Driver Assistance and Safety Warning Systems: Framework and Algorithms

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