With the rapid evolution of self-driving car technology, software engineers have an ever-increasing and crucial role to play. Currently, the main issue around self-driving vehicles is that of safety — one that no one except engineers can solve. This challenge of ensuring reliable technology that can improve efficiency on the road by eliminating human error is being taken up with urgency by most companies in the business.

Another area of focus in the autonomous vehicle industry includes the integration of 5G mobile communication into vehicles, thus improving sensors on such cars for recording, transmission, and analysis. Here too, software engineers are essential. From technology-driven changes in processes to increased job demand, the autonomous vehicle industry will have a huge impact on the lives of software engineers. Let’s take a look at what this might look like.

Process Change and Adaptation

As technology advances, a change in processes is inevitable. Two necessary changes that software engineers need to adapt to include Machine Learning and Continuous Integration.

Machine Learning

Machine learning is one of the most important aspects of robotics and technology today. As explained by Maryville University, machine learning is a special branch of artificial intelligence that deals with developing programs that are self-evolving. A machine-learned system can identify and integrate new data on its own and become more accurate. Thus, machine learning provides for minimum to no human input by building an automated prediction model. Needless to say, such technology is very valuable for the development of self-driving cars; especially since these cars require massive and complex data analysis that can continuously upgrade itself. Machine learning can benefit the automated vehicle industry in the following three ways:

• Automation: An automated system can eliminate the need to manually optimize the different steps of a viable prediction model. This will simplify the processes involving data scientists, data analysts, and DevOps and greatly reduce resources used.
• Cybersecurity: The time spent analyzing large data sets to identify potential security threats is cut down to less than one-fourth using machine learning. This is especially important since a cyber attack or hack on a self-driving vehicle could have disastrous consequences.
• Natural Language Processing (NLP): NLP deals with language analysis whereby human speech is broken down and compared to pre-recorded voices which AI can translate and interpret, thereby helping humans communicate with technology. It can also analyze speech and adapt to unfamiliar language, thereby improving human/AI interaction. Its potential to allow completely seamless communication may greatly help in developing automated vehicles.

Machine learning engineering is a quickly developing and lucrative career with respect to the developing self-driving car technology. In close collaboration with computer science and software development, it can drastically improve the current technological scenario. Coding and machine learning are vital for aspiring software engineers.

Continuous Integration

Software engineers must also adapt to Continuous Integration (CI). Appnovation defines CI as the “practices of merging development code in a frequent and continuous manner, from what used to be once a week or even once every few months to sometimes several times a day.”

This automated system brings about holistic thinking through continuous tests and constant bug fixing. CI is very useful in shortening the time between development and integration and/or regression testing, by identifying technical glitches quicker and more effectively.

Simply put, continuous testing leads to the instant identification of problems that can then be rectified in the initial stages themselves. Faster feedback cuts down on cost, improves predictability, and provides for developers to spend minimum time on fixing bugs.

Technology such as self-driving cars relies on handling large and frequent data updates. In this industry, changes to individual units must be made immediately, and all components must work together as smoothly as possible. Because of an autonomous vehicle’s reliance on real-time troubleshooting, software engineers must be well-versed with Continuous Integration methods.

Increased Jobs and Growth

According to an article in Marketwatch, the self-driving car market will lead to global revenues of USD 173.15 billion by 2023, with a compound annual growth rate (CAGR) of 36.2%. This growing industry is going to lead to an increase in demand for professionals with specialized training in automobile technology. Software engineers are undoubtedly the most sought after as the whole idea of driverless cars leans heavily on programming, coding, and making algorithms. With the demand for driverless technology jobs steadily rising, potential self-driving car engineers need to master new technological skills sooner rather than later. The duties of software engineers who are relevant in the current self-driving car landscape might include:

• Preventing and blocking hacks into the system and recovering from such hacks should they occur.
• Devising artificial intelligence that evolves itself to cater to changing demands of users.
• Developing driverless technology software in an environmentally-friendly way.
• Integrating hardware components of the driverless car to a software interface that is user-friendly.
• Devising artificial intelligence that evolves itself to cater to changing demands of users.

Self-driving cars are expected to highly improve the experience of driving and commuting by being user-friendly, disability friendly, lessening pollution, and reducing road accidents caused by human error. This will mean the continuous development and updating of technology by specialists in the automobile industry. Considering this, software engineers will play an increasingly important role in the self-driving car industry.