The automotive industry is speeding towards a technology-driven future powered by advances in computer vision. As vehicles become increasingly automated, connected, and electrified, computer vision serves as the eyes that allow cars to perceive and understand their surroundings.
This visual intelligence unlocks new levels of safety, efficiency, and convenience that stand to benefit automakers, drivers, and society as a whole. However, realizing this potential also raises complex technical challenges and ethical questions that the industry must thoughtfully navigate.
In this post, we’ll explore the top applications of computer vision in automotive and the business impact this technology enables. We’ll also consider some of the legal and social implications posed by more automated and intelligent vehicles.
A Vision for the Future: What is Computer Vision and How Does it Work?
Before diving into specific use cases, let’s briefly review what computer vision is and how it functions.
Computer vision refers to algorithms that can identify, process, analyze, and understand digital images and videos. Using advanced machine learning and deep neural networks modeled after the human visual cortex, computer vision systems can perform complex visual tasks like object classification, motion tracking, image segmentation, anomaly detection, and more.
In the context of automotive applications, some of the key capabilities enabled by computer vision include:
-
Object Detection – Identifying pedestrians, vehicles, traffic signs, and other objects on the road or in a vehicle’s surroundings. This allows properly equipped vehicles to understand their environment.
-
Semantic Segmentation – Labeling each pixel of an image according to what object it represents. This provides precise positional information for navigation and maneuvering.
-
Image Recognition – Categorizing entire images based on their contents. For example, determining if an image captures a congested highway vs. a quiet suburban street.
-
Video Processing – Analyzing real-time footage from cameras mounted on a vehicle to support dynamic decision making as surroundings change.
These core building blocks power most current and emerging computer vision use cases in the automotive sector. And as datasets grow and algorithms advance, what’s possible with computer vision will only continue expanding.
Driving Towards Autonomy: Computer Vision for Self-Driving Cars
Perhaps the most transformative application of computer vision in automotive is enabling autonomous or self-driving vehicles.
Fully autonomous cars require extremely sophisticated computer vision capabilities. The vehicle must continuously monitor feeds from multiple high-resolution cameras and other sensors, detecting and classifying all nearby objects, forecasting trajectories, assessing risk, and planning navigation – all in real-time.
Computer vision delivers the visual scene understanding required for self-driving cars to operate safely at higher speeds and across diverse environments, from bustling city streets to dusty rural roads.
Most experts agree fully autonomous vehicles remain years away from mass adoption. However, active research programs at tech giants like Tesla and Google along with sizable investments from major automakers signal the industry’s confidence in this technology.
In fact, analysts predict the market for autonomous driving technology will swell from $27 billion in 2022 to $60 billion by 2026. Computer vision sits at the core of enabling this growth.
And in the interim, automakers are steadily deploying computer vision-based advanced driver assistance systems (ADAS) that incrementally introduce autonomous functionalities like automatic emergency braking, lane keeping assist, self-parking, and more. These stepping stone technologies pave the way for fully self-driving cars using the same computer vision core.
Preventing Accidents: Computer Vision for Collision Avoidance Systems
Building on the theme of ADAS technologies, another major use case for computer vision is preventing collisions and accidents.
According to the NHTSA, over 90% of serious crashes are caused by human error. Computer vision enables active safety measures that compensate for human limitations.
For example, automatic emergency braking uses cameras and radar to detect impending collisions combined with computers to brake faster than humanly possible. Similarly, other collision avoidance features like blind spot monitoring, lane departure warnings, and cross traffic alerts all leverage computer vision for expanded perception and responsiveness.
These accident prevention applications of computer vision could save over 25,000 lives annually if widely deployed according to an Insurance Institute for Highway Safety study.
Governments are also responding through mandates and consumer safety programs designed to accelerate adoption of these technologies. For instance, the EU requires all new cars have autonomous emergency braking installed starting in 2022.
As cameras and sensors become cheaper and more ubiquitous in vehicles, computer vision-based safety systems will play an increasingly vital role in reducing accident rates industry-wide.
Manufacturing Made Smarter: Computer Vision Enables Automotive Automation
On the production side, computer vision also intersects with automotive manufacturing. As vehicles become more digitally connected and electrified, automakers are turning to smart factory solutions under the banner of Industry 4.0.
Here, computer vision drives automation across the assembly line, optimizing production speed, precision, and quality control. Conveyor belt cameras guide robots to weld vehicle frames to tolerances within mere millimeters. Other stations use computer vision for tasks like verifying torque specifications, installing electronics, checking tire tread, and assessing paint jobs visually.
Additionally, computer vision analyzes manufacturing analytics, forecasts mechanical issues, oversees inventory, and assists human workers as needed via collaborative robots.
The benefits of computer vision automation for automotive manufacturing include:
- Reduced defects and recalls
- Improved supply chain resilience
- Faster production throughput
- Lower long-term operating costs
- Safer working conditions
As investment in smart factories accelerates, computer vision adoption will scale in tandem, especially among leading automakers. GM, Toyota, Volkswagen, Hyundai, and BMW already operate highly automated plants and moreOEMs are following suit.
Monitoring Driver Behavior and Responding to Distraction
Another promising application is using in-vehicle cameras and computer vision algorithms to analyze driver behavior. These driver monitoring systems serve two key functions:
- Detecting distraction – Identifying activities like phone use or drowsiness that indicate impaired attention
- Adapting vehicle operation – Intervening through warnings or by engaging corrective systems like lane keeping assist
Driver state and behavior monitoring has potential to significantly curb accidents. But it also raises privacy issues regarding cameras continuously tracking vehicle occupants.
Manufacturers attempt to balance safety andsensitivity through measures like abstracting raw footage into outputs modeling attention only. However, more transparency and consent controls may prove necessary given the intimacy of the driving environment.
Government guidelines and independent oversight around acceptable use cases for in-cabin cameras will likely emerge to align business goals with consumer expectations.
Mapping the Road Ahead: Challenges, Considerations and What’s Next
As this overview illustrates, computer vision is transforming automotive across diverse fronts from R&D around autonomous driving to shop floor automation and in-vehicle safety. Further exponential growth seems inevitable.
However, realizing the full potential will require overcoming key challenges around training robust computer vision models and validating their safety. Regulatory schemes governing self-driving vehicles also remain in flux. Consumer acceptance and infrastructure preparedness also represent open questions.
On the ethics front, ensuring privacy and security of driver data emerges paramount as sensor fusion and connectivity become more pervasive. Transparency around use of occupant-facing cameras also deserves attention alongside functionality.
Overall the automotive sector seems poised on the cusp of a new era defined by vehicle automation and intelligence. Computer vision sits centered within this landscape as the technology making self-aware mobility possible.
Where the road ultimately leads depends on the wisdom and values of automakers and policymakers as much as the capabilities of Silicon Valley. But the speed and scale of transformation enabled by computer vision appear inevitable and profound.