An expert perspective on careers in Autonomous Driving technology

Autonomous Driving (AD) technology involves building software and systems that can mimic and improve upon human skills and judgement of driving a car. The software must not only perceive the environment and make basic decisions while driving but also use advanced technologies like AI, ML, DL , etc. to ensure that there are zero accidents. Safety standards across the world will increasingly require vehicles to have Advanced Driver Assistance Systems (ADAS) and AD capabilities.

Four senior KPITians playing pivotal roles in nurturing talent and spearheading KPIT’s Autonomous Driving (AD) technology development for global OEMs and Tier 1s share insights into what it takes to make it big in the AD space. Read on to find out more.

How does one lay a strong foundation for a successful career in AD software development?

As a relatively new area, AD software development is an attractive career avenue for candidates from a variety of backgrounds. These backgrounds include automotive engineering, aerospace, medical equipment, big data, gaming, cloud-based technologies, etc. The job demands a high level of technical competence.

For a newbie, a good place to start in AD software development is with training on past projects and cutting one’s teeth on Verification and Validation (V & V). Engineers then move on to Feature Development projects. In parallel, candidates who have a flair for Embedded Programming can explore it as well.

The areas of System Engineering and Functional Safety need a deeper system understanding and domain expertise, and engineers are brought on board these projects late. It usually happens after they build expertise and domain knowledge in software engineering and across AD areas, such as Vehicle Dynamics, Control Systems, Test Engineering and System Engineering.

Candidates can also acquire skills and gain competence in the latest technology areas, such as Virtual Simulation, Multicore, Machine Learning, Deep Learning and ROS so that they can build practical AI AD solutions.

Onsite client projects, simulations and vehicle tests with some of the biggest auto companies in the world, along with working with the latest processors, GPUs and state-of-the-art equipment like sensors (RADARs, LIDARs, Stereo and Mono Cameras, etc.) are also a part of the growth journey.

What are the essential skills to work on large Autonomous Driving production programs?

Manufacturers will move to SOP – start of production once the concepts and prototypes are approved. In this critical phase, every component that is being developed has to be production- ready.

The key software development expertise needed in this phase is shown in the following infographic (Exhibit 2)

Exhibit 2 – The infographic provides information about the key software development skills required in the production program phase. Copyright is with KPIT.

What should engineers focus on to work on global programs?

AD is a safety-critical application, and errors could mean life or death situations. The clear objective is “Zero Defect Delivery.” Given this backdrop, engineers need to develop a mindset of code craftsmanship: pursuing excellence and developing clean code. This kind of mindset requires a fundamental shift in engineering education. Laying the foundation for code craftsmanship requires an emphasis on problem-solving, strong domain understanding and an attitude of owning the entire software value chain from requirements to testing and productions. It goes beyond mastering tools and languages and focuses on getting the job done right. It is the direction engineers need to take to make a mark globally.

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This point of view has been created with insights from senior executives responsible for the AD Practice, Global Customers, Development of Solutions, and Recruitment at KPIT Technologies Ltd. KPIT has originally authored this point of view for TechGig.

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