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Now-a-days, Advanced driver-assistance systems (ADAS) is equipping cars and drivers with advance information and technology to make them become aware of the environment and handle potential situations in better way semi-autonomously. High-quality training and test data is essential in the development and validation of ADAS systems which lay the foundation for autonomous driving technology. ADAS uses the technology like radar, vision and combinations of various sensors including LIDAR to automatize dynamic driving tasks like steering, braking, and acceleration of vehicle for controlled and safe driving. And to integrate these advance technologies, the ADAS needs labeled data to train the algorithm to detect the various objects and moments of driver. Image annotation is one the well-known service to create such training data for computer vision. There are number of open source annotated datasets available viz. COCO, KITTI etc.

Active control mainly comprises of three parts; sensor-detects the input disturbance, actuator -provide counter measures and control logic -processing of input disturbances and converting it into logical output. Lot of methods for active vibration control are available but this paper deals with active control of steering wheel vibrations of an LCV. A steering wheel is, one such component that directly transfers vibration to the driver. Active technique described here is implemented using accelerometer sensor, IMA (Inertial Mass Actuator) and feed forward Fx-LMS (Filtered reference Least Mean Square) control algorithm. IMA is a single-degree-of-freedom oscillator. To enable a control, IMA needs to be coupled to the structure at a single point, acting as an add-on to the passive system. Fx-LMS is a type of adaptive algorithm which is computationally simple and it also includes compensation for secondary path effects by using an estimate of the secondary path.

In year 2015, 17 people were killed every hour by road accidents in India [1]. The occurrence of road accidents is observed to be higher during night, when visibility is at its lowest. The two factors which affect visibility are insufficient illumination and glare caused by the oncoming traffic. The Adaptive Front Lighting System [AFS] is an active safety feature which addresses these problems by employing specific lighting modes for Town, Country, Expressway conditions and automatic switching between Driving Beam and Passing Beam whenever required. Matrix of LEDs or a Projector with an actuator or a combination of both is employed in achieving different Lighting modes. The projector based AFS module is preferred for implementing the AFS control logic for passing beam owing to its economic cost.

On-board diagnostics (OBD) is a term referring to a vehicle's self-diagnostic and reporting capability. It is a system originally designed to reduce emissions by monitoring the performance of major emission related components. There are two kinds of on-board diagnostic systems: OBD-I and OBD-II. In India OBD I was implemented from April 2010 for BS IV vehicles. OBD II was implemented from April 2013 for BS IV vehicles. Apart from the comprehensive component monitors, OBD II system also has noncontinuous monitors like Catalyst monitoring, Lambda monitoring, and other after treatment system monitors. For OBD II verification and Validation, it is required to test all the sensors and actuators that are present in the engine, for all possible failures. From an emissions point of view there are lists of critical failures that are caused due to malfunction of sensors and actuators. Carrying out the full matrix failure testing on the running engine could be tedious, unsafe and time consuming.

Design of vehicle for targeted customer usage is one of the key steps during vehicle development process. Due to globalization, most of vehicles, aggregates, components are being designed for global market considering worldwide load spectrum. Generally for doing this the vehicle response is being measured for different markets but this process is very time consuming. Also for getting these vehicle dependent parameters, exercises need to be repeated on each type/class of vehicle. So there is a need to have a robust procedure, tools which will helps OEM’s to predict the loads, vehicle response for different market segments at an early stage of vehicle development program using the inputs which are vehicle independent. The solution for this could be to use vehicle independent input such as digitized road profiles (2D or 3D) of target customer markets in combination with proper MBD simulation tools.