Predicting driver maneuvers by learning holistic features

Ohn-Bar, Eshed; Tawari, Ashish; Martin, Sujitha; Trivedi, Mohan M.

doi:10.1109/ivs.2014.6856612

Cited by 51 publications

(23 citation statements)

References 17 publications

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“…Alternatively, more than a camera can be used to implement the tracking [51,61,62,63], that is, a distributed camera system is commonly used, where two or more cameras can be located inside the car cockpit. Following this line of research, in [61], they proposed a distributed camera framework for gaze estimation using head pose dynamics based on the algorithm proposed in [51].…”

Section: Face and Facial Landmarks Detectionmentioning

confidence: 99%

“…When tracking is lost, due to either the loss of facial point detection or the rejection of the estimated points, reinitialization is performed using a scoring criterion. In [62], they also used a two-camera system to overcome challenges in head pose estimation, which allows for continuous tracking even under large head movements, as proposed in [51]. Therefore, following the setup of [51] , a two-camera system can provide a simple solution in order to improve tracking during large head movements.…”

Section: Face and Facial Landmarks Detectionmentioning

confidence: 99%

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Driver Distraction Using Visual-Based Sensors and Algorithms

Fernández¹,

Usamentiaga

Carús³

et al. 2016

Sensors

105

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Driver distraction, defined as the diversion of attention away from activities critical for safe driving toward a competing activity, is increasingly recognized as a significant source of injuries and fatalities on the roadway. Additionally, the trend towards increasing the use of in-vehicle information systems is critical because they induce visual, biomechanical and cognitive distraction and may affect driving performance in qualitatively different ways. Non-intrusive methods are strongly preferred for monitoring distraction, and vision-based systems have appeared to be attractive for both drivers and researchers. Biomechanical, visual and cognitive distractions are the most commonly detected types in video-based algorithms. Many distraction detection systems only use a single visual cue and therefore, they may be easily disturbed when occlusion or illumination changes appear. Moreover, the combination of these visual cues is a key and challenging aspect in the development of robust distraction detection systems. These visual cues can be extracted mainly by using face monitoring systems but they should be completed with more visual cues (e.g., hands or body information) or even, distraction detection from specific actions (e.g., phone usage). Additionally, these algorithms should be included in an embedded device or system inside a car. This is not a trivial task and several requirements must be taken into account: reliability, real-time performance, low cost, small size, low power consumption, flexibility and short time-to-market. The key points for the development and implementation of sensors to carry out the detection of distraction will also be reviewed. This paper shows a review of the role of computer vision technology applied to the development of monitoring systems to detect distraction. Some key points considered as both future work and challenges ahead yet to be solved will also be addressed.

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Section: Face and Facial Landmarks Detectionmentioning

confidence: 99%

Section: Face and Facial Landmarks Detectionmentioning

confidence: 99%

Driver Distraction Using Visual-Based Sensors and Algorithms

Fernández¹,

Usamentiaga

Carús³

et al. 2016

Sensors

105

View full text Add to dashboard Cite

show abstract

“…In the predened case there is a set of patterns and the predictor uses sensor data to select that which reects the current operation and base the prediction on that pattern. This type includes identication or classication of driving type [34,41] or situation [58,74]. In the evolving pattern case the predictor tries to nd recurring structures in the data, and base the prediction on these structures.…”

Section: Predictionmentioning

confidence: 99%

Optimal Predictive Control of Wheel Loader Transmissions

Nilsson

2015

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The cover illustrations show wheel loaders at work. The transmissions of present heavy wheel loaders are in general based on torque converters. The characteristics of this component suits these machines, especially in that it enables thrust from zero vehicle speed without risk of stalling the engine, without active control. Unfortunately, the component also causes losses which might become large compared to the transmitted power. One approach for mitigating these losses is to switch to a continuously variable transmission.Changing to such a system greatly increases the possibility, and the need, for actively selecting the engine speed, and here a conict emerges. A low engine speed is desired for high eciency but a high speed is required for high power.Heavy wheel loaders often operate according to a common repeating pattern known as the short loading cycle.This cycle is extremely transient, which makes the choice of engine operating point both important and dicult. At the same time, the repeating pattern in the operation enables a rough prediction of the future operation. One way to use the uncertain prediction is to use optimization techniques for selecting the best control actions. This requires a method for detecting the operational pattern and producing a prediction from this, to formulate a manageable optimization problem, and for solving this, and nally to actually control the machine according to the optimization results.This problem is treated in the four papers that are included in this dissertation.The rst paper describes a method for automatically detecting when the machine is operating according to any of several predened patterns. The detector uses events and automata descriptions of the cycles, which makes the method simple yet powerful. In the evaluations over 90% of the actual cycles are detected and correctly identied. The detector also enables a quick analysis of large datasets. In several of the following papers this is used to condense measured data sequences into statistical cycles for the control optimization.In the second paper dynamic programming and Pontryagin's maximum principle is applied to a simplied system consisting of a diesel engine and a generator. Methods are developed based on the maximum principle analysis, for nding the fuel optimal trajectories at output power steps, and the simplicity of the system enables a deeper analysis of these solutions. The methods are used to examine and visualize the mechanisms behind the solutions at power transients, and the models form the basis for the models in the following papers.The third paper describes two dierent concepts for implementing dynamic programming based optimal control of a hydrostatic transmission. In this system one load component forms a stochastic state constraint, and the concepts present two dierent strategies for handling this constraint. The controller concepts are evaluated through simulations, in terms of implementability, robustness against uncertainties in the prediction and fuel savings. generell kunskap om hur m...

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“…Nonetheless, CRF on its own may not capture sub-structure in the temporal data well, which is essential for our purposes. By employing latent variables, the Latent-Dynamic CRF (LD-CRF) [12,15] improves upon the CRF and also provides a segmentation solution for a continuous data stream.…”

Section: Temporal Modelingmentioning

confidence: 99%