The DRIVE-SAFE project: signal processing and advanced information technologies for improving driving prudence and accidents

In this paper, we will talk about the Drivesafe project whose aim is creating conditions for prudent driving on highways and roadways with the purposes of reducing accidents caused by driver behavior. To achieve these primary goals, critical data is being collected from multimodal sensors (such as cameras, microphones, and other sensors) to build a unique databank on driver behavior. We are developing system and technologies for analyzing the data and automatically determining potentially dangerous situations (such as driver fatigue, distraction, etc.). Based on the findings from these studies, we will propose systems for warning the drivers and taking other precautionary measures to avoid accidents once a dangerous situation is detected. In order to address these issues a national consortium has been formed including Automotive Research Center (OTAM), Koç University, Istanbul Technical University, Sabancı University, Ford A.S., Renault A.S., and Fiat A. Ş

Experiments on decision fusion for driver recognition

In this work, we study the individual as well as combined performance of various driving behavior signals on identifying the driver of a motor vehicle. We investigate a number of classifier fusion techniques to combine multiple channel decisions. We observe that some driving signals carry more biometric information than others. When we use trainable combining methods, we can reduce identification error significantly using only driving behavior signals. Classifier combination methods seem to be very useful in multi-modal biometric identification in a car environment

Multimodal person recognition for human-vehicle interaction

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

Finite-State Vector Quantization for Block-Matching Motion Estimation

this paper, we present a novel vector quantizer (VQ) based architecture for BMA offering sizeable reduction in the number of computations and also the number of motion vectors transmitted. We also compare the performance of the proposed algorithm with other commonly used fast search methods

Digital Signal Processing for In-Vehicle Systems and Safety

Compiled from papers of the 4th Biennial Workshop on DSP (Digital Signal Processing) for In-Vehicle Systems and Safety this edited collection features world-class experts from diverse fields focusing on integrating smart in-vehicle systems with human factors to enhance safety in automobiles. Digital Signal Processing for In-Vehicle Systems and Safety presents new approaches on how to reduce driver inattention and prevent road accidents. The material addresses DSP technologies in adaptive automobiles, in-vehicle dialogue systems, human machine interfaces, video and audio processing, and in-vehicle speech systems. The volume also features: Recent advances in Smart-Car technology – vehicles that take into account and conform to the driver Driver-vehicle interfaces that take into account the driving task and cognitive load of the driver Best practices for In-Vehicle Corpus Development and distribution Information on multi-sensor analysis and fusion techniques for robust driver monitoring and driver recognition Knowledge distribution for vehicle-to-vehicle and vehicle-to-infrastructure communications Digital Signal Processing for In-Vehicle Systems and Safety  is useful for engineering researchers, students, automotive manufacturers, government foundations and engineers working in the areas of control engineering, signal processing, audio-video processing, man-machine interfaces, human factors and transportation engineering