The United Nations anticipates that while fully autonomous cars are still years away from becoming commonplace on roads, internationally agreed-upon regulations for their use could be finalized by mid-2026.
According to the UN, safety considerations and the substantial development costs of advanced systems have contributed to the gradual progress in autonomous vehicle technology. However, efforts to establish regulatory frameworks are progressing steadily.
"While the hype may be slowly disappearing, the actual serious work on regulation is advancing," UNECE's Deputy Executive Secretary, Dmitry Mariyasin, stated.
Road Regulations in Place
Richard Damm, Chair of the Working Party on Automated/Autonomous and Connected Vehicles (GRVA), announced that new UN regulations for driver assistance systems were adopted in February and are set to be enforced starting September 2024.
Looking ahead, Damm stated, "we are planning to have a globally harmonized regulation on Automated Driving Systems (ADS) ready for mid-2026.”
Unlike some sectors where regulations lag behind technology adoption, GRVA Secretary, François Guichard, emphasized that establishing rules ahead of widespread implementation aligns with historical practices in automotive advancements.
Guichard noted that regulations for electric vehicles were established prior to market introduction, and a framework is now in place for hydrogen vehicles should the industry move towards large-scale production.
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It is worth noting that major global automakers are currently introducing concept vehicles that incorporate relaxation zones equipped with advanced healthcare technology. These automotive innovations utilize various sensors to support drivers and passengers in maintaining optimal well-being throughout their travels.
Levels of Automation
Automation spans five levels, with the lower tiers already widely-adopted. At Level 2 (L2), drivers are required to maintain attention when navigating traffic; at Level 3 (L3), the driver is not actively driving when automated systems are engaged but must be prepared to take control when prompted; Level 4 (L4) allows for limited autonomous operation in specific areas, reducing the need for immediate driver intervention; at Level 5 (L5), fully-autonomous vehicular capabilities are present.
Level 1 and 2 technologies are likely present in about half of all new vehicles while L3 remains uncommon, and L4 isn't available for mass production.
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From a telecom industry perspective, L4 autonomous networks have become a strategic goal in the digital transformation of mainstream carriers, helping them transform from communications service providers (CSPs) into digital service providers (DSPs).
An example of this is Huawei’s Network Digital Map which has helped nearly 100 global customers in accelerating their transition to intelligent networks. Achieving the AN Level 3.8—the highest in the industry—it is equipped with the industry's first large network model.
‘Safe and Sound’ on the Road
GRVA’s Damm expressed optimism about the future of autonomous cars, while acknowledging that, “It is still some years ahead before we see it in the mass market because we have to resolve several issues."
Ensuring public safety remains the foremost priority in establishing regulatory frameworks for autonomous vehicles as the industry transitions into a new era of transportation. With approximately 1.2 million road traffic fatalities and 50 million injuries annually worldwide, the broader deployment of connected and autonomous vehicles holds significant potential for reducing accidents.
UNECE’s Mariyasin commented, "We don't know yet whether autonomous vehicles will be more of a problem or more of a solution.”
Connected cars promise enhanced safety by enabling efficient navigation through communication with traffic signals and road infrastructure. This technology ensures that drivers are prepared to adjust their speed in response to changing conditions (such as approaching red signals).
Moreover, at the core of self-driving cars are machine learning (ML) algorithms—sophisticated cognitive engines that enable vehicles to learn, adapt, and make decisions in dynamic driving environments. Analyzing these algorithms reveals both the capabilities and limitations of autonomous systems, ultimately reflecting their reliability in navigating complex and unpredictable scenarios.
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