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Prasad Shingne

I’m an autonomous driving and ADAS engineer with 16 years of academic and industry experience in simulation, validation, and vehicle systems. I’m currently a Staff Systems Engineer on the L4 Trucking Autonomy team at Applied Intuition, where I work on simulation, validation and safety.

Across my career, I’ve focused on building simulation-based validation that lets autonomy teams rely less on physical testing and ship with more confidence. That includes leading ADAS simulation at Lucid Motors, scaling L4 off-road validation at Applied Intuition, and earlier work at Audi, Byton, and Ford.

I hold a PhD in Mechanical Engineering from the University of Michigan, am a certified SOTIF Engineer (ISO 21448:2022, TÜV SÜD Academy), and completed Stanford GSB’s LEAD program in 2026. Outside of work, I build perception and localization systems on embedded hardware and write about technology and history on Substack.

2026 -

In August 2026, I returned to Applied Intuition as a Staff Systems Engineer on the Trucking Autonomy systems engineering team. My focus is on closing the sim-to-real gap for L4 autonomous trucking. That means making simulation results better predict how the system behaves on the road, and reducing the number of issues that escape simulation testing and are first found during on-road testing.

This role builds on my earlier work at Applied on off-road autonomy and on simulation at Lucid. The goal is the same: catch problems in simulation before they reach a real vehicle.

2025 -

2026

At Lucid, I was one of three simulation leads for ADAS and owned simulation test orchestration end to end. I turned features into test cases, kept up the scenario library, and drove a “simulate before physical test” approach across the team. That simulation infrastructure supported DreamDrive Pro and Lucid’s move toward L2++ driving.

I acted as technical deputy to the simulation engineering manager. I directed two engineers on scenario creation and led an external supplier team building the test orchestration tooling. I owned key parts of the SiL and HiL validation build-out, which was tied to a company-level goal on simulation adoption, and led readiness reporting for the first ADAS HiL system built under the NVIDIA partnership.

I also built a log replay tool that turns drive logs into reusable simulation scenarios, co-developed a CARLA tool that creates scenes from any OpenStreetMap region, and led SOTIF supplier selection from the validation side.

2020 - 2022

I joined Audi's newly established Automated Driving Development R&D office in San Jose, CA, in April 2020 as one of its first ten employees. As a Senior Development Engineer for ADAS, I focused on systems engineering for SAE Level 2 hands-free and Level 3 driving assist technologies, testing and validating the current Level 2 system to identify corner cases, diagnose root causes, and propose solutions. I also worked on software prototyping and integration, providing rapid feedback to developers on system performance. In addition to these technical responsibilities, I played a key role in building the team at the new office, interviewing over ten candidates and hiring several developers.

2018 - 2020

Byton was a Chinese-German all-electric vehicle brand founded by former BMW and Nissan executives. I joined their Santa Clara, CA R&D office in January 2018 to contribute to the development of the fully electric M-Byte SUV. As a Technical Lead in Powertrain Systems Analysis, I played a key role in driving the M-Byte’s performance and energy attributes toward SOP targets. My work involved leveraging modeling, simulation, dyno testing, and on-road testing to assess system performance, identify gaps, and implement improvements. Collaborating cross-functionally, I worked with engineering teams across powertrain and chassis to enhance performance, while also engaging with management and marketing teams to provide updates and ensure accurate external messaging.

2015 - 2018

I joined the Ford Research and Innovation Center in Dearborn, MI, in June 2015 as an Electrified Propulsion Systems Research Engineer within the Vehicle Systems Analysis Group. In this role, I led several modeling, simulation, and analysis projects, providing critical upfront input to new hybrid and electric vehicle programs, resulting in significant cost savings for the company. My work included developing a new combustion engine model, which, combined with on-premises HPC, reduced calibration time for a new engine program by an order of magnitude. I also coupled production control code with a powertrain model to diagnose and explain performance and efficiency issues identified during testing. Additionally, I utilized vehicle usage data from various sources to define electrification requirements for large Ford vehicles.

2008 - 2015

I began graduate school in Mechanical Engineering at the University of Michigan, Ann Arbor, in 2008, where I worked in the Auto Lab under the guidance of Professors Dennis Assanis, Claus Borgnakke, and Jason Martz. My research was part of a $12 million multi-university project funded by the U.S. Department of Energy (DOE) and Robert Bosch LLC, aimed at demonstrating the commercial feasibility of Homogeneous Charge Compression Ignition (HCCI) technology for production engines. Using CFD and engine dynamometer testing, I explored the underlying physics of HCCI combustion. This work culminated in my dissertation, where I developed a faster-than-real-time 0D model of HCCI combustion, designed to serve as a plant model for powertrain controls applications.

2003 - 2007

I earned my Bachelor’s degree in Mechanical Engineering from the Vishwakarma Institute of Technology, University of Pune. Beyond laying the foundation for my engineering career, this is where I began exploring product development by participating in and hosting various competitions focused on building cars, robots, and electro-mechanical systems.