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In order to travel in a chaotic and dynamic environment, an autonomous vehicle requires a motion plan. This motion plan ensures collision free, optimum travel without violating any traffic rules. The optimum solution for path planning problem exists in higher dimensions, however, with the help of useful heuristics the problem can be solved in real time, which is required for real time operation of an autonomous vehicle. There are different well established techniques available to plan a collision free kinematically traversable path. One of such techniques is called conformal Lattice planner. However, the legacy version of conformal lattice planner is not optimized and also is prone to fail under specific dynamic environment conditions. Moreover, the legacy version of conformal lattice planner is also not road aware. Due to this reason it is a semi optimized way to solve the motion planning problem.

Autonomous Emergency Braking (AEB) systems play a critical role in ensuring vehicle safety by detecting potential rear-end collisions and automatically applying brakes to mitigate or prevent accidents. This paper focuses on establishing a framework for the Verification & Validation (V&V) of Advanced Driver Assistance Systems (ADAS) by testing & verifying the functionality of a RADAR-based AEB ECU. A comprehensive V&V approach was adopted, incorporating both virtual and physical testing. For virtual testing, closed-loop Hardware-in-Loop (HIL) simulation technique was employed. The AEB ECU was interfaced with the real-time hardware via CAN. Data for the relevant target such as the target position, velocity etc. was calculated using an ideal RADAR sensor model running on the real-time hardware. The methodology involved conducting a series of test scenarios, including various driving speeds, obstacle types, and braking distances.

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.

The Indian market for battery-powered electric vehicles (xEV) is growing exponentially in the coming years, fueled by tumbling lithium-ion battery prices and favorable government policies. Lithium-ion battery is leading in clean mobility ecosystem for electric vehicles. LiBs efficient and safe performance for tropical climatic conditions is one of the primary requirements for xEV to succeed in India. The performance of LiBs, however, is impacted due to ambient temperature as well as the heat generated within cell due to the load cycle electrochemical reaction. The acceptable operating temperature region for LiBs normally is between 20 °C to 45 °C and anything outside of this region will lead to degradation of performance and irreversible damages. Therefore, understanding the thermal behavior is very crucial for an efficient battery thermal management.