The automotive industry is undergoing a revolution with the shift to electric and increasingly autonomous vehicles (AVs). In the US, AVs such as robo-taxis are increasingly commonplace. In Asia, numerous pilots are underway to accelerate the technology. Various pilots are underway in Singapore, including self-driving buses and taxis within a dedicated testing area. In Malaysia, an AV pilot on a 5G highway is taking place and Japan has recently allowed nearly autonomous vehicles on public roads in a limited capacity. All of these initiatives are helping accelerate the drive for more autonomy.

Much of the appeal of AVs is the potential to make driving safer by reducing traffic, crashes, injuries and fatalities. However, before level 5 autonomy can be achieved, it’s vital to develop trust in autonomous driving, but this is a complex problem with no margin for error. Adding to the challenge, the ecosystem involved in building and developing AVs is more complex and distributed than the established auto industry.

Unless trust is built within the industry and society by overcoming the web of safety concerns, the drive toward fully AVs will remain in the slow lane. Below are some of the constraints throttling progress:

Overcoming Data and Security Hurdles

Vehicles today have an array of sensors and in-vehicle networks, and all of the data collected is helping make them more predictable. Digital twin solutions enable simulations to evaluate the performance in realistic scenarios, fostering an environment where AVs can make accurate, predictable and safe real-time decisions. However, to make full autonomy possible, the industry must collaborate to create a shared data framework supporting more intelligent modeling to help address the complex safety concerns with driverless cars.

As vehicles have more and more software embedded, this is increasing vulnerabilities that hackers can and will exploit. With AVs, the consequences range from the loss of financial or personal data or, in the worst-case scenario, potentially life-threatening. Therefore, ensuring every possible loophole is identified and addressed in the design phase is critical to delivering a safe and secure autonomous driving experience. In addition, once the car is on the road, every software or system update must be subject to rigorous testing to ensure it doesn’t introduce security flaws.

Navigating the Regulatory Environment

Regulations are essential to accelerate autonomy and acceptance of AVs.

With the majority of accidents a result of human error, AV legislation has the opportunity to help usher in an era of safer driving. However, the entire ecosystem and supply chain must be subject to these edicts to build trust. Advancing AVs requires collaboration and implementing various standards—for example, what is the minimal viable, safe vehicle?

Work is underway to ensure autonomous driving is subject to regulations with initiatives including the UN’s World Forum for Harmonization of Vehicle Regulations. In Asia, Singapore was the first country to put in place regulations by amending its Road Traffic Act to recognize that vehicles don’t require human drivers. In April, level 4 autonomous driving or fully automated driving is now permitted under certain conditions in Japan. As regulations expand, this will help address safety concerns and build trust.

Technology: Problem & Solution

Technology’s sheer complexity is part of the problem and the solution. Software-defined vehicles are an essential destination on the road to full autonomy. And the technology race in vehicles is creating vast data streams that can aid development by improving deterministic decision-making. As intelligent software becomes commonplace in vehicle and driver assistance systems, this will help minimize human error—creating safer vehicles.

However, more technology is required to test that the AV performs exactly as expected, meeting the necessary safety standards under every scenario. From traversing snow on the Hokkaido Expressway to the congested streets of Manila, each potential permutation requires evaluation before autonomous driving becomes ubiquitous. This is no small feat.

Digital twins layered with innovative technologies, including AI, machine learning and virtual reality, are essential as the simulation enables every component and system, spanning hardware and software, to be evaluated. This approach ensures that the car meets safety and quality standards before hitting the assembly line. In addition to saving money and time, this is the only realistic way to test and evaluate each model under the multitude of environments and situations the vehicle may be in.

The Autonomous Future

Addressing the areas outlined will make the journey to more autonomous vehicles less bumpy and more direct. However, rather than suddenly pivoting to fully autonomous driving, the ecosystem will continue to make incremental pit stops toward the destination.

The risks associated with autonomous vehicles (AVs) require all parties to work together to resolve them. And the level of collaboration will determine the pace at which they are overcome. Given the complexity of achieving complete autonomy, attempting to predict the exact timeline is futile. However, as the software arms race continues, by 2030, we should have a clearer understanding of when a secure and autonomous future will come into view.

By: DocMemory
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