This paper considers a comprehensive and collaborative project to collect large amounts of driving data on the road for use in a wide range of areas of vehicle-related research centered on driving behavior. Unlike previous data collection efforts, the corpora collected here contain both human and vehicle sensor data, together with rich and continuous transcriptions. While most efforts on in-vehicle research are generally focused within individual countries, this effort links a collaborative team from three diverse regions (i.e., Asia, American, and Europe). Details relating to the data collection paradigm, such as sensors, driver information, routes, and transcription protocols, are discussed, and a preliminary analysis of the data across the three data collection sites from the U.S. (Dallas), Japan (Nagoya), and Turkey (Istanbul) is provided. The usability of the corpora has been experimentally verified with a Cohen's kappa coefficient of 0.74 for transcription reliability, as well as being successfully exploited for several in-vehicle applications. Most importantly, the corpora are publicly available for research use and represent one of the first multination efforts to share resources and understand driver characteristics. Future work on distributing the corpora to the wider research community is also discussed.

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Next-generation vehicles will undoubtedly feature biometric person recognition as part of an effort to improve the driving experience. Today's technology prevents such systems from operating satisfactorily under adverse conditions. A proposed framework for achieving person recognition successfully combines different biometric modalities, borne out in two case studies. O ver the past 30 years, the field of biometric person recognition-recognizing individuals according to their physical and behavioral characteristics-has undergone significant progress. Next-generation human−vehicle interfaces will likely incorporate biometric person recognition, using speech, video, images, and analog driver behavior signals to provide more efficient and safer vehicle operation, as well as pervasive and secure in-vehicle communication. Yet, technical and deployment limits hamper these systems' ability to perform satisfactorily in real-world settings under adverse conditions. For instance, environmental noise and changes in acoustic and microphone conditions can significantly degrade speaker recognition performance. Similarly, factors such as illumination and background variation, camera resolution and angle, and facial expressions contribute to performance loss in visually identifying a person. Biometric person recognition in vehicles is especially likely to challenge researchers because of difficulties posed by the vehicle's interior compartment as well as by economics.In this article, we present an overview of multimodal in-vehicle person recognition technologies. We demonstrate, through a discussion of our proposed framework, that the levels of accuracy required for person recognition can be achieved by fusing multiple modalities. We discuss techniques and prominent research efforts, and we present the results of two case studies we conducted. The sidebar, "Solutions for In-Vehicle Person Recognition," discusses related work. Why in-vehicle person recognition?To improve the driving experience, making it better and safer, manufacturers are making vehicles smarter. As vehicles become smarter, information processing from vehicle sensors will become more complex, as will vehicle personalization, which involves adapting the driver and passenger compartment for safer driving and travel.Personalization features will, for example, increase vehicle safety by determining whether the person behind the wheel is an authorized driver (say, the vehicle's legal owner). If so, the driver will be able to operate the vehicle. If the individual isn't authorized, the vehicle will prevent operation and could communicate with authorities to report the incident and initiate an enforcement procedure, such as preventing the driver from starting the ignition.Personalization will promote safe driving by monitoring a driver's behavior. Once the human−vehicle interface has identified a driver and determined road and traffic conditions, the vehicle can monitor the driver's behavioral signals from braking, accelerating, or swerving. With these signals, the hum...

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