The automotive industry is gearing up for a major transformation driven by advanced technologies like computer vision and artificial intelligence. As a machine learning expert with over 15 years of experience applying AI in the automotive sector, I foresee computer vision playing a pivotal role across the automotive value chain in 2024 and beyond.
1. Autonomous Driving
Self-driving cars enabled by computer vision cameras, sensors and robust AI algorithms undoubtedly form the most popular and revolutionary application of this technology.
The autonomous vehicles are fitted with multiple high-resolution cameras and sensors all around the vehicle. The cameras act as the ‘eyes‘ of the car, continuously scanning the environment in real-time. Sophisticated computer vision algorithms analyze the visual data to detect lanes, read traffic signs, identify pedestrians, objects and other vehicles on the road. The AI system processes all this visual information along with inputs from other sensors to make optimal driving decisions and navigate the vehicle safely.
Companies like Tesla, Waymo, GM Cruise are at the forefront of developing and testing autonomous vehicles equipped with cutting-edge computer vision and AI capabilities. According to Allied Market Research, the global market for autonomous vehicles is forecast to skyrocket from $54.23 billion in 2019 to a whopping $556.67 billion by 2026.
Autonomous vehicle enabled by computer vision (Image credit: Research.ai)
I believe we will see accelerating progress in autonomous vehicle technology backed by advances in computer vision, neural networks and simulation capabilities. This will steadily expand the operational design domains for self-driving cars – from geo-fenced areas to urban roads and eventually highways. However, it may take 5-10 years before fully autonomous vehicles without human drivers/supervisors become commercially available.
2. Advanced Driver Assistance Systems (ADAS)
Modern vehicles are increasingly being equipped with Advanced Driver Assistance Systems (ADAS) powered by computer vision, such as automatic emergency braking, lane keep assist, lane departure warning etc.
Cameras on the vehicle constantly scan the surroundings and feed visual data to the ADAS system. Computer vision algorithms analyze the video, detect risks and alert the driver or even take action autonomously (like braking) if needed.
With ADAS features becoming mandated in the US, Europe and other regions, automakers like Toyota, Volvo, BMW are actively incorporating computer vision-enabled ADAS capabilities into their vehicle models. This significantly expands the scope and impact of computer vision technology in the automotive sector.
According to ResearchAndMarkets, the global ADAS market is predicted to exhibit robust growth – from $27 billion in 2020 to over $83 billion by 2027.
3. Automated Quality Inspection
Automakers are leveraging computer vision for automated visual inspection on the factory floor to detect defects, measure components, check paint jobs and more. This enables finding issues early, reducing costly recalls and improving overall quality.
For example, BMW uses computer vision-based camera systems to inspect whether the right model number badge is affixed on each manufactured vehicle. Ford utilizes AI-powered camera systems that can scan thousands of points on each vehicle body and alert operators in case of defects or deviations from design specs.
Per McKinsey research, automated visual inspection powered by computer vision and deep learning can improve quality rates by 10-40%, compared to traditional manual inspection processes. This can deliver huge cost savings for automotive manufacturers.
4. Driver & Occupant Monitoring
Automakers are increasingly adopting in-cabin driver and occupant monitoring systems equipped with computer vision software, cameras and sensors.
These systems use gaze tracking and facial recognition algorithms to constantly monitor the driver‘s attention, distraction levels, drowsiness/fatigue so that alerts can be issued to avoid accidents when the driver is not focused.
Moreover, during vehicle crash tests, occupant monitoring systems track the occupant kinetics and injury patterns in a crash using computer vision video analytics. The biomechanical data provides valuable insights to design safer vehicle interiors.
According to Fortune Business Insights, the global driver monitoring system market alone is expected to reach $2.5 billion by 2027, highlighting the growth in this application.
5. Robotic Automation in Manufacturing
The automotive sector is driving increasing adoption of computer vision-enabled robotics and automation on the manufacturing floor across functions like welding, material handling, quality control and more.
For instance, KUKA‘s AI-powered robots can handle delicate tasks like inserting rubber seals around car windows with precision – a complex job for human workers. Hyundai‘s state-of-the-art factory in Alabama deploys 500 robots infused with computer vision for optimal production and minimal errors.
McKinsey estimates that automotive plants can improve productivity by 10-25% through computer vision automation. This creates a massive incentive for automakers to continue deploying these technologies.
In summary, computer vision is bringing transformative advancements spanning autonomous driving, ADAS, quality assurance, safety systems, and manufacturing automation in the automotive space. As algorithms and hardware continue advancing rapidly, I expect more innovative and high-impact use cases of this technology to emerge in 2024 and beyond. Automotive companies need to proactively leverage computer vision and AI to build smarter, safer and higher quality vehicles to stay competitive. Those who lag in adoption risk being disrupted by nimble startups and tech giants accelerating innovation in this domain.