Human Behavior Based Predictive Brake Assistance

McCall, J.C.; Trivedi, Mohan M.

doi:10.1109/ivs.2006.1689597

Cited by 27 publications

(16 citation statements)

References 10 publications

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“…In both situations, the goals of safety and comfort often conflict with each other [25]. McCall and Trivedi proposed human-behavioral-based predictive braking assistance using a Bayesian network to reduce rear-end collisions in [26]. They used lidar and wheel speed sensors to detect the surrounding situation for braking and used a foot camera and a head camera to detect driver intent and behavior.…”

Section: B Vehicle Reversing Speed Control Algorithmsmentioning

confidence: 99%

A Novel Vehicle Reversing Speed Control Based on Obstacle Detection and Sparse Representation

Zhang

Zhifeng

et al. 2015

IEEE Trans. Intell. Transport. Syst.

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In this paper we present a vehicle safety method for reversing speed control based on obstacle detection and sparse representation. The proposed system consists of three main steps, namely, binocular camera obstacle detection and segmentation, obstacle tracking and recognition, and vehicle reversing speed control algorithm. First of all, a binocular camera system is used to detect obstacles as a vehicle is reversing. Using disparity computation and triangulation, we can get all objects' distance information in the rear of a vehicle. Second, the framework of particle filter and sparse representation are used to track and identify the main obstacles such as human or animal bodies, vehicles, or any other objects. Finally, the vehicle reversing speed control algorithm, which controls the electronic throttle opening and automatic braking prior to collisions, makes the reversing control safer and more reliable. This system has been field tested on a Dodge sport utility vehicle by which the final performance evaluation demonstrates the validity of the proposed vehicle reversing speed control.Index Terms-Binocular camera system, obstacle detection, obstacle tracking and recognition, particle filter, sparse representation, vehicle reversing control.

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Section: B Vehicle Reversing Speed Control Algorithmsmentioning

confidence: 99%

A Novel Vehicle Reversing Speed Control Based on Obstacle Detection and Sparse Representation

Zhang

Zhifeng

et al. 2015

IEEE Trans. Intell. Transport. Syst.

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“…Results show that lane changes on highways can be predicted at least 2.5s in advance with good accuracy. A similar approach is presented in [14], concerning the braking behavior prediction in a car-following scenario. The authors predict the need for braking depending on the scene observation as well as the intention of braking depending on the observation of the driver.…”

Section: Related Workmentioning

confidence: 99%

“…The knowledge about the predicted future behavior of the driver can be used to detect a dangerous behavior, as presented in [14]. Another possible application is the use of this prediction to anticipate and start an early braking of the car.…”

Section: Future Workmentioning

confidence: 99%

Behavior prediction at multiple time-scales in inner-city scenarios

Ortiz

Fritsch

Kummert

et al. 2011

2011 IEEE Intelligent Vehicles Symposium (IV)

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Abstract-We present a flexible and scalable architecture that can learn to predict the future behavior of a vehicle in inner-city traffic. While behavior prediction studies have mainly been focusing on lane change events on highways, we apply our approach to a simple inner-city scenario: approaching a traffic light. Our system employs dynamic information about the current ego-vehicle state as well as static information about the scene, in this case position and state of nearby traffic lights.Our approach differs from previous work in several aspects. First of all, we hold that predicting the precise sequence of physical and actuator states of a car driving in dynamic innercity traffic is both challenging and unnecessary. We therefore represent predicted behavior as a sequence of few elementary states, termed behavior primitives. As a second aspect, behavior prediction is treated as a multi-class learning problem since there are multiple behavior primitives. Rather than causing problems, we show that this fact can be exploited for computing information-theoretic measures of prediction confidence, thereby allowing to identify and reject unreliable predictions.We show that the horizon of predictions can be extended up to 6s, and that uncertain predictions can be detected and eliminated efficiently. We consider this a significant result since typical prediction horizons are usually in the range of 1 to 2s.The main message of this paper is that simple learning methods can achieve excellent prediction quality at long time horizons by operating purely on the "system-level", i.e., using an abstract, low-dimensional situation representation. Since the learning approach greatly reduces the design effort, and since we show that the prediction of multiple behavior classes is feasible, we expect our architecture to be scalable to more complex scenarios in inner-city traffic.

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“…Sparse Bayesian learning was used to estimate the probability density functions based on the observations. The complete description of this system is given in [35].…”

Section: ) Lane-change-intent Analysismentioning

confidence: 99%

Looking-In and Looking-Out of a Vehicle: Computer-Vision-Based Enhanced Vehicle Safety

Trivedi¹,

Gandhi²,

McCall

2007

IEEE Trans. Intell. Transport. Syst.

262

123

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Abstract-This paper presents investigations into the role of computer-vision technology in developing safer automobiles. We consider vision systems, which cannot only look out of the vehicle to detect and track roads and avoid hitting obstacles or pedestrians but simultaneously look inside the vehicle to monitor the attentiveness of the driver and even predict her intentions. In this paper, a systems-oriented framework for developing computervision technology for safer automobiles is presented. We will consider three main components of the system: environment, vehicle, and driver. We will discuss various issues and ideas for developing models for these main components as well as activities associated with the complex task of safe driving. This paper includes a discussion of novel sensory systems and algorithms for capturing not only the dynamic surround information of the vehicle but also the state, intent, and activity patterns of drivers.

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Human Behavior Based Predictive Brake Assistance

Cited by 27 publications

References 10 publications

A Novel Vehicle Reversing Speed Control Based on Obstacle Detection and Sparse Representation

A Novel Vehicle Reversing Speed Control Based on Obstacle Detection and Sparse Representation

Behavior prediction at multiple time-scales in inner-city scenarios

Looking-In and Looking-Out of a Vehicle: Computer-Vision-Based Enhanced Vehicle Safety

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