Insecure and inaccessible code can discourage investment in connected vehicles and limit innovation | So Good News
Automakers are using artificial intelligence (AI) to create personalized user experiences in connected vehicles. In March 2022, Google’s Waymo revealed that cities such as San Francisco will soon offer driverless ride-hailing services – an indication of how the use of AI technology has grown as a result of companies such as Google, Amazon, Apple, Microsoft and others. entering the car market.
It’s clear that manufacturers strive to deliver the best user experience (UX), but vehicle safety and regulations need to be carefully scrutinized. As this technology becomes more widely accepted, software developers will see it as a new mechanic.
In the UK, the government says driverless cars will be on UK roads by 2025, a short timescale that raises questions about how quickly regulations will change and how autonomous vehicles (AVs) will develop. Connected cars are like mobile phones – consumer demand for new features is increasing and software updates are needed to deliver them. But rapid development without proper regulation and testing increases the risk of vulnerabilities in the source code of those vehicles. This was seen in 2015 when security researchers shocked the auto industry by breaking into a Jeep driven by a (consensual) tech journalist.
There is a real threat to innovation and investment in the sector, stemming from premature regulation and consumer demand for the rapid development of autonomous vehicles.
Putting AI in the driver’s seat
As AI becomes more integrated and integral to improving car UX, we will start to see original equipment manufacturers (OEMs) adopting the technology both inside and outside the vehicle. AI is being used to improve manufacturing, vehicle design, testing and supply chain management. If we look closely at Waymo’s self-driving car, we can see that the use of AI and machine learning (ML) will have a significant impact on the industry. The need for continuous real-time access to multiple versions of Google’s datasets is a daunting task, especially given that both the underlying software and all versions of the dataset require testing.
In many areas of AI today, there are not necessarily immediate security concerns. Typically, it is used to improve UX for cloud-based navigation, speech recognition, weather and surface recognition, with a focus on vehicle safety. But if navigation fails, fatal accidents and collisions can occur. Therefore, a basic advanced driver assistance system (ADAS) to regularly rebalance AI/ML systems is essential for car safety.
Developer mechanics — safety, standards and regulations
Historically, the use of AI in vehicles has been subject to strict regulations that have slowed the pace of innovation. The pace of technological progress outpaces existing regulations, which has prevented developers from implementing AI safely and securely. However, increasing consumer demand for connected devices and vehicles has seen their numbers grow by 270 percent over the past five years, meaning OEMs are under pressure to implement AI quickly.
As we approach a period of rapid development and deployment of AI driven by customer demand, the main topic of conversation has shifted to code complexity and regulations requiring vehicle safety. Although the leading function of AI implementation is not to improve security-critical features and focuses on user experience, it is important to remember that code security cannot be neglected.
For developers, it is important to understand that security concerns regarding code vulnerabilities due to widespread unavailability are exacerbated by the constant need to update software. Without this access to the source code, developers cannot identify weaknesses and security issues arise. As such, access to the source code of the tools and runtime software used in the development process is essential, and visibility of your designs for use in the vehicle is equally important.
Automotive cybersecurity and its future in the cloud
Despite the adoption of AI/ML technologies, the reliance on cloud-based technologies and data in vehicles will increase. As we approach a future where all vehicles on the road will be connected, the cloud-based aspects of autonomous vehicles must be embraced in order to survive. Of course, during testing and implementation, safety features such as local ADAS based on LiDAR, radar and cameras will be needed to guarantee system safety.
Aggressive adoption of open source software continues to pose potential threats, such as native hacking. As the mechanics of the future, developers need access to a wide range of development tools, and governing bodies need to apply appropriate regulation. Especially considering that consumer expectations for updated software and regularly updated features will only continue to grow.
Commercially licensed open source software will undoubtedly become more common. Access to the source code of the tools and runtime software used in the development process and seeing the designs created for use in vehicles are as important to efficiency in product development. Commercial licensing and IP protection are an absolute necessity, along with making the underlying source code available to the developer community.
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Patrick Shelley is a Manager Solutions Engineer The Qt Company