Less Traffic, Shorter Delivery Times: How This AI Future is Being Blocked by Automakers

Less Traffic, Shorter Delivery Times: How This AI Future is Being Blocked by Automakers

AI Solutions for Traffic Issues Hindered by Lack of Standards

Artificial Intelligence (AI) can solve significant traffic issues; however, the lack of standardization among manufacturers leads to challenges in implementation.

The Potential of AI to Transform Traffic Efficiency

In a world increasingly focusing on sustainability and efficiency, the mobility sector faces the challenge of finding innovative solutions: optimizing traffic flow, reducing congestion, and minimizing environmental impact. AI plays a key role, with the potential to tackle these issues and fundamentally change how we commute.

Current Utilization of AI in Traffic Management

AI is already being used in various areas of traffic management. For example, AI-powered surveillance systems analyze traffic flow in real-time, identifying and predicting congestion. Cities like Barcelona and Singapore are using such technologies to make traffic more efficient, with studies showing that AI in traffic control can improve traffic flows by up to 35 percent.

German Startups Leading the Way

The German startup Isarsoft measures traffic and pedestrian flows to support planners with data and analysis for infrastructure measures, including traffic flow and city parking utilization. The software integrates with existing camera systems, meaning cities do not have to overhaul their entire IT infrastructure and hardware.

Reduced Congestion Through AI Traffic Management

AI-driven traffic management systems can significantly reduce congestion. A study from the University of Cambridge estimates that intelligent traffic systems can decrease average travel times by up to 20 percent, saving time for commuters and significantly reducing CO2 emissions due to less time spent in traffic.

AI-Optimized Traffic Flow Maximizes Road Capacity

The time savings in an AI-optimized traffic flow are substantial, meaning roads can be better utilized. More vehicles can pass through a bottleneck during peak times, benefiting logistics companies by shortening delivery times.

The Evolution Towards Direct Data Sharing From Vehicles

While existing AI systems rely on data from cameras, the future aims for direct data sharing from the vehicles themselves through “Vehicle-2-X Communication.” In this system, vehicles send data directly to traffic management systems, where predictive software can forecast traffic flow and relay information back to the vehicles.

Challenges with Vehicle-to-Everything (V2X) Communication

However, V2X Communication is still in its infancy. Some manufacturers’ vehicles can communicate with each other, but not with the infrastructure yet. Qualcomm demonstrated a pilot in Las Vegas limited to a few intersections. The main issue is that municipalities are unwilling to bear the costs of building the infrastructure.

Cost and Infrastructure Hurdles in Implementing V2X Technology

These costs are significant as each traffic signal would require an installed and maintained receiver. Additionally, there’s a lack of consensus among automakers on a common transmission and broadcasting standard, with some favoring a WLAN variant and others a 5G mobile standard. Without agreement, expanded AI functionality will not advance.

AI in Traffic Control: A Step Towards Sustainable and Efficient Mobility

Integrating AI into traffic control is not a cure-all, but it is a crucial step towards more efficient and environmentally friendly mobility. The challenges related to costs and implementation are considerable, but the potential benefits — economic and ecological — are immense. It is time for cities and traffic authorities to seriously consider this technology to meet modern mobility challenges.

Don Dahlmann has been a journalist for over 25 years and has been in the automotive industry for over ten years. Every Monday, you can read his column “Drehmoment,” which offers a critical perspective on the mobility sector.
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