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The design and development of complete vehicle, understanding of chassis system development process is an important task. Chassis frame of a vehicle is supporting member, both structurally and functionally, to all other chassis aggregate systems viz. suspension, steering, braking system etc. In this paper, a methodology for chassis frame model construction and validation is explained. In present work, chassis frame model is validated in terms of modal parameters and also against static loading conditions. Existing chassis 3D Computer Aided Design (CAD) data was generated using scanning and cloud point data conversion technique. FE model was generated and validated through experimental measurements viz. modal testing, vertical bending, lateral bending, and torsional bending test. Loading and boundary conditions were replicated on the complete FE model in CAE domain and test validation was carried out using appropriate mesh biasing and weld modeling techniques.

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.