**1. Introduction**

In order to enhance road safety as well as to satisfy increasingly stringent government regulations in western countries, automobile makers are confronted with incorporating a range of diverse technologies for driver assistance to their new model. These technologies help drivers to avoid accidents, both at high speeds and for backward movement for parking. This system can be placed into the category of advanced driver-assistance systems (ADAS). Besides increasing safety, ADAS [1] applications are concerned with to enhancing comfort, convenience, and energy efficiency. It is emerging as new driving technology supported with Adaptive Cruise Control, Automatic Emergency Brake, blind spot monitoring, lane change assistance, and forward collision warnings etc. It is an important platform to integrate these multiple applications by using data from radar, lidar, and ultra sound sensors etc. The vehicle engine related to hardware such as actuators, engine, brake, steering get the commands from the above sensors to enable the ADAS to take desired actions with respect to alerting the driver for detection of hazardous object or location or stopping the vehicle if necessary. For example, the recognition of black spot warning, lane change assistance and forward collision warning are extremely becoming useful in the ADAS.

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Vehicles.

*Models and Technologies for Smart, Sustainable and Safe Transportation Systems*

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During the gradual emergence of Connected and Automated vehicle (CAV), driver behavior modeling (DBM) coupled with simulation system modeling appears to be an instrumental in predicting driving maneuvers, driver intent, vehicle and driver state, and environmental factors, to improve transportation safety and the driving experience as a whole. These models can play an effective role by incorporating its desired safety-proof output into Advanced Driver Assistance System (ADAS. To cite an example, it could be said with confidence that the information generated from all types of sensors in an ADAS driven vehicle with accurate lane changing prediction models could prevent road accidents by alerting the driver ahead of time of potential danger. It is increasingly felt that DBM developed by incorporating personal driving incentives and preferences, with contextual factors such as weather and lighting, is still required to be refined, calibrated and validated to make it robust so that it turns into more better personalized and generic models. In regard to the modeling of personalized navigation and travel systems, earlier studies in this area have mainly considered ideal knowledge and information of the road network and environment, which does not seem to be very realistic. More researches are required to be conducted to address this real life challenges to make ADAS more acceptable to society.

There are an increasing evidences from the various literatures that a single vehicle making inferences based on sensed measurement of the driver, the vehicle, and its environment is mostly focused for DBM where there is any hardly attempt made to develop DBM in the traffic environment in the presence of vehicle to vehicle (V2V), and vehicle to infrastructure (V2I) scenario- communications system. It would be interesting to develop DBM with respect to connected and automated vehicle (CAV) to leverage information from multiple vehicles so that more global behavioral models can be developed.. This would be useful to apply the output of the CAV modeling in the design of ADAS driven vehicle to create a safety proof driving-scenario for diverse applications.
