The automotive industry is undergoing a technological revolution with artificial intelligence (AI) as the driving force. As AI’s impact on vehicle design and production increases, it is also paving the way for advanced operational efficiency.
AI delves into a myriad of dynamic factors, including ever-changing traffic conditions, weather variations, speed limit restrictions, and more. It also extends to a broader spectrum of advancements in the industry.
AI and ML: A dynamic duo
Traditional approaches are in gridlock when faced with real-time, dynamic changes on the road. This is where AI and machine learning (ML) bring a suite of capabilities to navigate the convoluted networks of modern-day transportation.
As industries – including automotive – gradually unravel the layers of AI and ML, the horizon of achievable efficiencies continues to expand. Here are three areas business leaders should focus on.
Real-time traffic and predictive analytics
AI offers a dynamic solution that goes beyond mere reroutes and schedule adjustments. By harnessing vast amounts of data – from GPS devices, traffic cameras, and more – AI facilitates real-time traffic analysis.
This approach allows dynamic route adjustments to bypass congested areas and ensures time-efficient travel. Moreover, ML analyzes historic traffic patterns to forecast potential bottlenecks and suggest alternative routes before they even become a real issue on the road.
Both Uber and Lyft use AI algorithms to pair customers with drivers while refining driver routes on-the-fly. This improves customer satisfaction by shortening the waiting period for passengers while also guaranteeing the driver follows the most efficient route.
Fleet and environmental considerations
AI fine-tunes the allocation of vehicles to tasks based on various factors. For a delivery company, AI can distribute vehicles based on their capacity and fuel efficiency, ensuring that a fuel-efficient vehicle is assigned to a long-distance delivery.
UPS uses a tool called ORION for the majority of its package deliveries. It leverages AI to optimize routes while conserving fuel and minimizing carbon emissions. Drivers can simultaneously and instantly access pre-arranged multi-stop delivery routes on their phones.
Meanwhile, AI and ML are used in BMW’s US plant, saving the company more than a million dollars yearly in production costs. Digital director at Mercedes-Benz AG, Axel Weichert, says they are “currently testing use cases across all business units and investigating their opportunities and risks.”
User experience
Personalization is another milestone on AI’s roadmap. Since delivery services now factor in customers’ preferred delivery windows, AI makes this task easier by adjusting routes when necessary.
Amazon’s routing algorithms serve as a real-world example. It factors in customers’ preferred delivery windows by plotting routes. These algorithms also analyze accident data and real-time driver behavior to make adjustments when necessary.
As autonomous vehicles (AVs) enter the scene, AI’s role will reach new levels since AVs rely heavily on ML. General Motors recently pledged $35 billion through 2025 towards electric and AVs, while Cruise is introducing driverless taxis in Japan.
The recent wave of investments underscores the industry’s move towards harnessing ML, but many remain in the dark.
Locate2u CEO Steve Orenstein believes AI will lead to a “significant shift” in business over the next decade. Orenstein explains: “There’s a lot of businesses today that don’t even understand what AI can do and the impacts that it can have on their business.”
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About the author
Cheryl has contributed to various international publications, with a fervor for data and technology. She explores the intersection of emerging tech trends with logistics, focusing on how digital innovations are reshaping industries on a global scale. When she's not dissecting the latest developments in AI-driven innovation and digital solutions, Cheryl can be found gaming, kickboxing, or navigating the novel niches of consumer gadgetry.
