Insights

Exploring the Intersection of Product Engineering and Automotive Trends

UST Product Engineering

Product engineering is a rapidly evolving process. While conceiving, designing, and deploying products is nothing new, how new products are engineered radically differs from previous iterations. Today, artificial intelligence (AI), augmented reality (AR), and cloud computing – to name a few – are giving product engineers tools that allow them to move faster, more effectively, and more cost-efficiently.

At the same time that the methods of product engineering have quickly transformed, certain legacy products have been advancing at a similar pace. The automotive industry is an excellent example of how intertwined these new trends in product engineering are with the product itself. Automobiles are mechanically complicated and have become more so with the pivot to electric vehicles (EVs), self-driving cars, and more connective human-machine interfaces (HMIs). All these have added a new layer of intricacy to these machines.

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Tackling automotive challenges

Because these overlapping areas are rapidly transforming, staying on top of their shared trends is important. Staying informed about cutting-edge innovation allows companies to offer competitive products to customers in a field with multiplying sophisticated products. On the flip side of market satisfaction, technologies come with new regulations, and manufacturers need to watch trends for regulatory purposes. Companies must balance their calculations – between customer satisfaction and regulatory compliance – to assess risks and navigate a rapidly changing tech-business ecosystem.

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Product engineering trends impacting the automotive industry

ADAS: Self-driving cars – or cars with advanced driver assistance systems (ADAS) – are increasingly becoming popular. These systems are predominantly built on AI and machine learning (ML), which enable these automobiles to make informed decisions about safety and surroundings and to learn better ways to operate continuously.

All forms of hardware like radar, lidar, and cameras help interpret the surroundings, while AI algorithms interpret the data from these systems. The continued innovation in AI and ML has not only impacted the availability of ADASs in automobiles but experiential data and ML have allowed the efficacy of these systems to advance rapidly.

Cloud Computing: The cloud has made sharing large quantities of information across geography and time possible. A transformative technology in nearly every workspace, it has allowed product engineering teams to collaborate more nimbly. It has made the entire product engineering process more innovative. Enhanced design and simulation capabilities have allowed teams to work dynamically to meet consumer needs.

Virtual prototyping and testing are the most direct ways cloud computing has given product engineers a highly effective tool for testing their ideas before major expenditures in manufacturing models. With so many systems at play – mechanical IoT data, material efficiencies and costs, and performance data – cloud computing allows automobile engineers to store the data remotely, accessing it as needed. This helps free up computing power for intensive modeling software. The evolution of AI features in the cloud also offers product engineers powerful tools for sifting through datasets efficiently.

Cybersecurity: Application-Specific Integrated Circuits (ASICs) are a type of semiconductor that is designed for specific tasks. As opposed to the more general-purpose semiconductors found in most consumer computers, ASICs allow for more efficient uses of energy and computing power to address specific needs. ASICs have primarily been developed to offer automobile-grade semiconductors to allow smart cars – or automobiles with advanced technological functions – to perform these computing-intensive functions.

However, there is the possibility of hacking where there is a semiconductor. Unfortunately, Car hacking is a growing threat to smart cars and necessitates parallel advancement in cybersecurity. Not only can smart cars be hijacked to control steering, acceleration, and other motor functions, but the personal data stored in smart systems is also a target. Engineers are focused on tackling these challenges. Encryption, intrusion detection, and prevention systems (IDPSs), and regular software updates have reduced the possibility of car hacking.

Electric Vehicles (EVs): In the last ten years alone, EV offerings have exploded from a few niche vehicles, hybrids, and Tesla to a mainstay of automotive manufacturing. The desire to reduce greenhouse gasses has fueled this pivot to entirely electric vehicles, but new challenges have come. How do you extend the driving range? How do we manufacture lighter materials to enable these extensions? What are the most cost-effective ways to engineer quality batteries and charging infrastructure?

AI, cloud computing, and AR have given product engineers an excellent toolkit for simulating and testing answers to these questions. By using data and intricate modeling, the engineering process has accelerated alongside consumer demand for EVs to deliver quality automobiles while doing so more cost-effectively.

Because EVs still exist in a less reliable fueling ecosystem than traditional combustion engine cars, there is a greater emphasis on strong navigation offerings in EVs. Because navigational offerings are usually coupled with other smart features, EVs have been mostly designed with smart car features.

V2X: Encompassing a wide array of offerings, Vehicle-to-Everything, or V2X, is an umbrella term. For instance, Vehicle-to-Grid (V2G) is the above-stated example whereby an EV is connected using GPS technology to a real-time map of potential charging stations. Connecting to a map also allows for Vehicle-to-Infrastructure (V2I), where traffic updates or toll booths become part of the navigational landscape. The same product engineering for both makes pairing them easy.

With the rise of ADAS vehicles, cars must navigate the space around them. Vehicle-to-Pedestrian (V2P) and Vehicle-to-Vehicle (V2V) technologies allow new automobiles to connect with the world around them, not only getting better information for their ADAS algorithms to work with but making alerts to the world around them, such as to the personal devices of nearby pedestrians.

Software updates for complex software and many other new product engineering features can come using Vehicle-to-Network (V2N) connectivity to local networks, allowing an automobile to update as a smartphone does. The product engineering that pioneered the latter applies to the automotive industry.

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Conclusion

Many product engineering trends that have fueled the growth and change in many other industries have applied to the automotive industry. Better testing and design practices are leading to more regular, customer-oriented offerings and updates, thus accelerating the adoption of more complex technologies for everyday use. Connect with an expert today to learn more about how product engineering impacts the automotive industry.

For more information visit UST Product Engineering