About

Adityaveer Raswan is a highly skilled software engineer with a specialization in automotive engineering, autonomous vehicles, robotics, and controls. With a Master's degree in Automotive Engineering from Clemson University International Center for Automotive Research, he has extensive experience in developing autonomous software stacks using C++ and Python on Robot Operating System (ROS) and testing and integrating them in Gazebo Simulation and on Autonomous Vehicle prototypes. Adityaveer is proficient in developing autonomous driving algorithms for Motion Planning, Parking Spot Detection fusing Camera/LIDAR data, and vehicle lateral tracking using Stanley, Pure-Pursuit, and Model Predictive Control. He also has knowledge of Deep Learning and Reinforcement Learning techniques for autonomous applications. Currently working as the Engines Department Head at Team Defianz Racing, Adityaveer has led his team in tuning the engine's existing steady-state and transient fuel map and ignition maps using DTA S80 Pro ECU on a Dynamometer using the LC2 Wideband Lambda Controller. He has designed the Honda CBR 600 F4i engine's intake and exhaust systems, fuel delivery system, and cooling systems. He was also responsible for wiring and data logging systems. Adityaveer has simulated the Honda CBR 600 F4i engine parameters on Ricardo Wave-Build to achieve 92% correlation to the chassis dynamometer results. He has used the validated model for evaluating the effect of changing intake lengths, plenum volumes, and exhaust runner lengths on volumetric efficiency (VE) and power curves. Adityaveer has previously worked as a Graduate Engineer Trainee and Senior Engineer at Bajaj Auto Limited. He has experience in testing the Automated Manual Transmission's algorithm and hardware and developing algorithms in C to validate and diagnose Automated Manual Transmission's control strategies and associated hardware having production level variations. He has also co-developed the ETC feedback-feedforward control algorithm for steady-state and transient control and improved control tracking by 3.2%. Adityaveer's tech stack includes Software Engineering, Reinforcement Learning, Python, Deep Learning, Integration, SVM, Object detection, Keras, OpenCV, Object Detection, and Scikit. With almost six years of relevant experience, Adityaveer is a highly skilled software engineer with a proven track record of developing autonomous software stacks and improving engine performance.