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The Dedicated Short-Range Communications (DSRC) standards suite is based on multiple cooperating standards mainly developed by the IEEE. In particular, we focus this paper on the core design aspects of DSRC which is called Wireless Access in Vehicular Environment (WAVE). WAVE is highlighted in IEEE 1609.1/.2/.3/.4. The DSRC and WAVE standards have been the center of major attention in both research and industrial communities. In 2008, WAVE standard was the third best seller standards in the history of the IEEE. This attention reflects the potential of WAVE to facilitate much of the vehicular safety applications. In this paper we present a fairly detailed tutorial of the WAVE standards. We extend the paper by describing some of the lessons learned from particular design approaches. We direct the reader to the landmark research papers in relevant topics. We alert the reader about major open research issues that might lead to future contribution to the WAVE design.

A Vehicle Make and Model Recognition (VMMR) system can provide great value in terms of vehicle monitoring and identification based on vehicle appearance in addition to the vehicles’ attached license plate typical recognition. A real-time VMMR system is an important component of many applications such as automatic vehicle surveillance, traffic management, driver assistance systems, traffic behavior analysis, and traffic monitoring, etc. A VMMR system has a unique set of challenges and issues. Few of the challenges are image acquisition, variations in illuminations and weather, occlusions, shadows, reflections, large variety of vehicles, inter-class and intra-class similarities, addition/deletion of vehicles’ models over time, etc. In this work, we present a unique and robust real-time VMMR system which can handle the challenges described above and recognize vehicles with high accuracy. We extract image features from vehicle images and create feature vectors to represent the dataset. We use two classification algorithms, Random Forest (RF) and Support Vector Machine (SVM), in our work. We use a realistic dataset to test and evaluate the proposed VMMR system. The vehicles’ images in the dataset reflect real-world situations. The proposed VMMR system recognizes vehicles on the basis of make, model, and generation (manufacturing years) while the existing VMMR systems can only identify the make and model. Comparison with existing VMMR research demonstrates superior performance of the proposed system in terms of recognition accuracy and processing speed.

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