ACV facts & figures
By 2030, 25% of miles driven in the U.S. could be in shared, self-driving electric cars.1 Apart from driverless cars, vehicle manufacturers and dealerships are also maximizing the power of AI to sell, and market cars, improve customer experience and make business easier and more efficient.
This article highlights ways AI technology, such as IoT data analytics, machine learning, natural language processing, cloud computing, and big data is revolutionizing the automotive industry.
How AI Is Being Used Across the Automotive Industry
AI in Automotive Manufacturing
As the automotive industry increasingly faces a stringent regulatory environment, vehicle manufacturers rely on AI technologies to improve the car-making process and meet compliance requirements. Today, AI systems can also collect and analyze visual data from sensors to identify defects in vehicle components and alert managers to prevent costly mishaps. AI-powered robots and automated systems are also used in assembly lines to perform repetitive tasks precisely and quickly. For example, AI-powered robots are employed to autonomously pick parts and perform welding and painting, minimizing human error and speeding up the entire manufacturing process.
One of the leading benefits of AI in the vehicle industry is enhanced safety. Through AI, vehicle manufacturers can incorporate top-notch safety features into everyday driving. AI-based systems, such as autonomous emergency braking (AEB), adaptive cruise control (ACC), and lane departure warning (LDW), help alert drivers of potential dangers on the road.
Specifically, AEB is designed to prevent or mitigate collisions by automatically applying brakes when a potential crash is detected. It leverages a combination of sensors, cameras, and radar technology to monitor the road environment for potential hazards.
Similarly, LDW uses cameras and sensors to monitor vehicle position within the lane and provide timely alerts to drivers if it detects unintended lane departure. This way, the driver is empowered to prevent deadly crashes from happening.
As consumer preferences change, an increasing number of car buyers today prefer a vehicle that allows them to express their ideals, hobbies, values, and beliefs on the road. To ensure they meet their customer's need for a personalized vehicle and driving experience, some leading manufacturers tap into the capabilities of AI during the design of vehicles. For instance, Porsche uses its machine learning configuration system, called the "Recommendation Engine," to arrive at vehicle packages that meet individual driver preferences.
AI in Automotive transport
AI is integral in enabling autonomous driving by providing the intelligence and decision-making capabilities needed for a vehicle to operate without human intervention. Self-driving cars have multiple sensors, including ultrasonic sensors, radars, lidars, and smart cameras.
These features can perceive the surrounding environment and provide data that AI algorithms process to identify and classify common objects on the road, such as other cars, pedestrians, road markings, and traffic signs. AI algorithms can also leverage data from GPS and other sensors to deliver accurate navigation, control, and mapping. This way, autonomous driving technology delivers a whole new experience for physically challenged drivers while enhancing safety, increasing mobility, and improving the traffic flow.
However, even with such alluring promise, there are several limitations and challenges in regards to autonomous vehicles that need to be addressed. According to a recently released Researchgate report dubbed Data Analytics: Paving the Way to Sustainable Urban Mobility1, some of the limitations that driverless vehicles pose range from privacy and data protection issues, deep ethical issues, inclement weather conditions, and traffic conditions and laws.
AI-powered driver assistance systems analyze massive amounts of data and provide crucial insights to enable drivers to make real-time decisions. These systems leverage sensors to assist with various aspects of driving, including steering and pedestrian detection. They also help monitor blind spots and alert the driver in real-time, allowing them to prevent road crashes.
AI driver monitoring combines computer vision, facial analysis, eye tracking, and machine learning techniques to assess the driver's current state and provide alerts on unsafe road behaviors. The AI-based monitoring system can also leverage cameras to capture real-time video of the driver's face and identify potential distractions, drowsiness, inattentiveness, and other habits that divert attention from driving.
AI in Automotive service
AI technology leverages data from diverse sources, including vehicle sensors, internal systems, external databases, and historical records, and applies advanced analytic techniques to provide insights that inform preventive maintenance practices. AI uses the power of IoT in automobiles to track the real-time conditions of vehicles and analyze mountains of vehicle performance data to identify deviations from normal patterns and alert drivers and fleet managers whenever maintenance is required. This consolidated data provides a comprehensive view of the vehicle's performance and operating conditions at any given time.
How AI Can Fuel Dealership Success
Today's dealerships are increasingly incorporating AI to fuel dealership success by improving customer experience, sales processes, and operational efficiency. Here are some ways AI can be used to revolutionize a dealership2.
Enhanced Customer Service
AI-powered chatbots and virtual assistants can revolutionize customer service in dealerships. These intelligent systems can interact with customers, answer inquiries, and offer personalized vehicle recommendations.
The chatbots leverage their natural language processing capabilities to ensure prompt and accurate assistance 24/7, improving customer satisfaction and increasing chances of repeat purchases.
Optimized Operational Efficiency
AI bots can automate multiple repetitive and time-consuming tasks to help streamline operations, remove human error and cut costs. For example, some dealerships use AI-powered software to manage their inventory and set competitive prices. By reducing the risk of overstocking or understocking, dealers can improve their sales and reduce costs.
Dealerships can use AI algorithms to analyze mountains of customer data, market trends, and historical sales records to create precise predictions and sales forecasts.
By tapping into machine learning techniques, dealerships can gain valuable insights into customer preferences, buying patterns, and potential leads. This information can help marketing teams identify potential customers and target specific segments with personalized marketing campaigns.
Register to Become an ACV Member Today
At ACV Auto, we understand the need for dealerships to up-skill their teams in line with the changing industry technology. Register today to become a member of ACV Auctions to learn more.
- ResearchGate. "Major Limitations and Concerns Regarding the Integration of Autonomous Vehicles in Urban Transportation Systems: Proceedings of 4th Conference on Sustainable Urban Mobility (CSUM2018), 24 - 25 May, Skiathos, Greece." URL: https://www.researchgate.net/publication/329581918_Major_Limitations_and_Concerns_Regarding_the_Integration_of_Autonomous_Vehicles_in_Urban_Transportation_Systems_Proceedings_of_4th_Conference_on_Sustainable_Urban_Mobility_CSUM2018_24_-_25_May_Skiatho.
- Itransition. 'AI in Automotive: How Artificial Intelligence is Transforming the Automotive Industry.' Itransition, www.itransition.com/ai/automotive.
Aug 30, 2023