17,096 research outputs found
Detecting Distracted Driving with Deep Learning
Ā© Springer International Publishing AG 2017Driver distraction is the leading factor in most car crashes and near-crashes. This paper discusses the types, causes and impacts of distracted driving. A deep learning approach is then presented for the detection of such driving behaviors using images of the driver, where an enhancement has been made to a standard convolutional neural network (CNN). Experimental results on Kaggle challenge dataset have confirmed the capability of a convolutional neural network (CNN) in this complicated computer vision task and illustrated the contribution of the CNN enhancement to a better pattern recognition accuracy.Peer reviewe
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An evaluation framework for stereo-based driver assistance
This is the post-print version of the Article - Copyright @ 2012 Springer VerlagThe accuracy of stereo algorithms or optical flow methods is commonly assessed by comparing the results against the Middlebury
database. However, equivalent data for automotive or robotics applications
rarely exist as they are difficult to obtain. As our main contribution, we introduce an evaluation framework tailored for stereo-based driver assistance able to deliver excellent performance measures while
circumventing manual label effort. Within this framework one can combine several ways of ground-truthing, different comparison metrics, and use large image databases.
Using our framework we show examples on several types of ground truthing techniques: implicit ground truthing (e.g. sequence recorded without a crash occurred), robotic vehicles with high precision sensors, and to a small extent, manual labeling. To show the effectiveness of our evaluation framework we compare three different stereo algorithms on
pixel and object level. In more detail we evaluate an intermediate representation
called the Stixel World. Besides evaluating the accuracy of the Stixels, we investigate the completeness (equivalent to the detection rate) of the StixelWorld vs. the number of phantom Stixels. Among many findings, using this framework enables us to reduce the number of phantom Stixels by a factor of three compared to the base parametrization. This base parametrization has already been optimized by test driving vehicles for distances exceeding 10000 km
Application of TRIZ to develop an in-service diagnostic system for a synchronous belt transmission for automotive application
Development of robust diagnostic solutions to monitor the health of systems and components to ensure through life cost effectiveness is often technically difficult, requiring an effective integration of design development with research and innovation. This paper presents a structured application of TRIZ and USIT (Unified Structured Inventive Thinking) to generate concept solutions for an in-service diagnostic system for a synchronous belt drive system for an automotive application. The systematic exploration through TRIZ and USIT methods has led to the development of six concept solution ideas directed at the functional requirement to determine the state or condition of the belt. The paper demonstrates that the combined deployment of TRIZ and USIT frameworks is a valuable approach addressing difficult design problem
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