For voice-enabled car navigation systems that use a multi-purpose cloud speech recognition service (cloud ASR), utterance classification that is robust against speech recognition errors is needed to realize a user-friendly voice interface. The purpose of this study is to improve the accuracy of utterance classification for voice-enabled car navigation systems when inputs to a classifier are error-prone speech recognition results obtained from a cloud ASR. The role of utterance classification is to predict which car navigation function a user wants to execute from a spontaneous utterance. A cloud ASR causes speech recognition errors due to the noises that occur when traveling in a car, and the errors degrade the accuracy of utterance classification. There are many methods for reducing the number of speech recognition errors by modifying the inside of a speech recognizer. However, application developers cannot apply these methods to cloud ASRs because they cannot customize the ASRs. In this paper, we propose a system for improving the accuracy of utterance classification by modifying both speech-signal inputs to a cloud ASR and recognized-sentence outputs from an ASR. First, our system performs speech enhancement on a user's utterance and then sends both enhanced and non-enhanced speech signals to a cloud ASR. Speech recognition results from both speech signals are merged to reduce the number of recognition errors. Second, to reduce that of utterance classification errors, we propose a data augmentation method, which we call “optimal doping,” where not only accurate transcriptions but also error-prone recognized sentences are added to training data. An evaluation with real user utterances spoken to car navigation products showed that our system reduces the number of utterance classification errors by 54% from a baseline condition. Finally, we propose a semi-automatic upgrading approach for classifiers to benefit from the improved performance of cloud ASRs.

Navigation systems providing route-guidance and traffic information are one of the most widely used driver-support systems these days. Most navigation systems are based on the map paradigm which plots the driving route in an abstracted version of a two-dimensional electronic map. Recently, a new navigation paradigm was introduced that is based on the augmented reality (AR) paradigm which displays the driving route by superimposing virtual objects on the real scene. These two paradigms have their own innate characteristics from the point of human cognition, and so complement each other rather than compete with each other. Regardless of the paradigm, the role of any navigation system is to support the driver in achieving his driving goals. The objective of this work is to investigate how these map and AR navigation paradigms impact the achievement of the driving goals: productivity and safety. We performed comparative experiments using a driving simulator and computers with 38 subjects. For the effects on productivity, driver's performance on three levels (control level, tactical level, and strategic level) of driving tasks was measured for each map and AR navigation condition. For the effects on safety, driver's situation awareness of safety-related events on the road was measured. To find how these navigation paradigms impose visual cognitive workload on driver, we tracked driver's eye movements. As a special factor of driving performance, route decision making at the complex decision points such as junction, overpass, and underpass was investigated additionally. Participant's subjective workload was assessed using the Driving Activity Load Index (DALI). Results indicated that there was little difference between the two navigation paradigms on driving performance. AR navigation attracted driver's visual attention more frequently than map navigation and then reduces awareness of and proper action for the safety-related events. AR navigation was faster and better to support route decision making at the complex decision points. According to the subjective workload assessment, AR navigation was visually and temporally more demanding.

XVC (eXtensible Viewer Composition) is an in-vehicle user interface framework for telematics applications. It provides a document-oriented application model, which enables drivers to simultaneously make use of multiple information services, while maintaining satisfactory control of their vehicles. XVC is a new client model that makes use of the beneficial functions of in-vehicle navigation devices. This paper presents the results from usability tests performed on the XVC framework in order to evaluate how the XVC client affects drivers' navigation while using its functions. The evaluations are performed using the Advanced Automotive Simulator System located at KATECH (Korea Automobile Technology Institute). The advantages of the XVC framework are evaluated and compared to a non-XVC framework. The test results show that the XVC framework navigation device significantly reduces the scanning time needed while a driver obtains information from the navigation device.

This letter presents a novel approach for traffic light detection in a video frame captured by an in-vehicle camera. The algorithm consists of rotated principal component analysis (RPCA), modified amplitude thresholding with respect to the histograms of the PC planes and final filtering with a neural network. The proposed algorithm achieves an average detection rate of 96% and is very robust to variations in the image quality.

In geographical databases for navigation, users raise various types of queries concerning route guidance. The most fundamental query is a shortest-route query, but, as dynamical traffic information newly becomes available and the static geographical database of roads itself has grown up further, more flexible queries are required to realize a user-friendly interface meeting the current settings. One important query among them is a detour query which provides information about detours, say listing several candidates for useful detours. This paper first reviews algorithms for the shortest and k shortest paths, and discusses their extensions to detour queries. Algorithms for finding a realistic detour are given. The efficiency and property of the algorithms are examined through experiments on an actual road network.