Abstract
Topic: Driver Assistance Technology is emerging as new driving technology popularly known as ADAS. It is 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 multifunction sensors, cameras, radars, lidars etc. and send command to plural actuators, engine, brake, steering etc. ADAS technology can detect some objects, do basic classification, alert the driver of hazardous road conditions, and in some cases, slow or stop the vehicle. The architecture of the electronic control units (ECUs) is responsible for executing advanced driver assistance systems (ADAS) in vehicle which is changing as per its response during the process of driving. Automotive system architecture integrates multiple applications into ADAS ECUs that serve multiple sensors for their functions. Hardware architecture of ADAS and autonomous driving, includes automotive Ethernet, TSN, Ethernet switch and gateway, and domain controller while Software architecture of ADAS and autonomous driving, including AUTOSAR Classic and Adaptive, ROS 2.0 and QNX. This chapter explains the functioning of Assistance Driving Technology with the help of its architecture and various types of sensors.
Highlights
advanced driver assistance systems (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
There are many demonstration seen on advanced vehicles up to Level 3 or more, so far automobile manufacturers have not been able to commercialize to the high level automated vehicle which requires detailed and comprehensive legislation in the countries
A number of fundamentals aspects of ADAS that are a part of the complex process of the system have been discussed
Summary
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. 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 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
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