You’ve probably heard of Alphabet’s self-driving car project, Waymo, and how it gathers vast amounts of data using LIDAR, radar, cameras, and sensors daily. But what happens to all this data? Well, the raw data is labeled to classify objects like pedestrians, other cars, road signs, traffic signals, and obstacles, and then fed into the AI models used in ADAS systems of autonomous vehicles to train them.
If the models are trained well, they can navigate the roads easily, prevent accidents, and analyze driving scenes. All in all, we can say that the success of its AI model lies in data annotation!
Automobile manufacturers are taking notes from here and producing next-gen AVs by making data annotation a priority so that their AVs can make precise decisions in dynamic real-world environments. If you want to know more about it, read this blog as we explore the role of data annotation in autonomous vehicles to ensure safety and reliability on the road.
How Data Annotation Enhances the Performance of Autonomous Vehicles?
According to a McKinsey and Company survey, 12% of new passenger cars sold in 2030 will have L3+ autonomous technologies, while 37% will have advanced autonomous driving (AD) by 2035. This is a significant leap from the current AVs on the market, a shift that will be made possible only through accurate data labeling for autonomous vehicles. The importance of high-quality labeled data will be crucial to:
- Navigate Roads Safely
At Level 3 and Level 4 of autonomous driving, most of the driving is done by vehicles, with drivers taking charge only for safety-critical functions. If the world is to move to these levels of AVs, the AI models used in ADAS systems need to be trained to interpret the road layout, obstacles, lane markings, and other environmental factors without much human interference. Precise data labeling allows AV systems to make real-time decisions regarding lane-keeping, speed control, and obstacle avoidance. Let’s see how specific data annotation techniques work here:
- Semantic Segmentation: Provides a detailed, pixel-by-pixel understanding of the scene so that AI models can differentiate between road markings (like the pedestrian zones or driving lanes) and weather conditions that affect road conditions (snow, ice, rain, or fog).
- Bounding Box Annotation: Draws rectangular boxes around objects of interest, such as vehicles, pedestrians, and road signs, to help AI models track specific objects rather than understand the context of the entire scene.
- Verify Passenger Identity for Enhanced Security
In the last few years, robotaxis has been gaining popularity across the US, with the global market expected to reach USD 45.7 billion by 2030. In such advanced AVs, verifying passenger identity will become critical to ensure both the safety of passengers and vehicles. By using annotated facial recognition data, the AI systems can make sure the right passenger is in the vehicle and they are comfortable. Here are some of the data annotation techniques used for the same:
- Landmark Annotation: AI models need to differentiate between people and ensure the correct one is onboarding. To train them, facial features such as the corners of the eyes, the tip of the nose, the corners of the mouth, the upper and lower lip, and the chin are labeled in the training dataset.
- Keypoint Annotation: Points are usually placed on important body parts of the passenger images (fingertips, knuckles, wrist joints, elbows, and shoulders) to identify a person’s pose. AI systems are then trained to identify whether the traveler is expressing anxiety or the desire for aid.
- Accident Prediction for Preventive Action
Just almost three years back, 392 Level 2 ADAS crashes were reported as of May 15, 2022. It sometimes puts a question mark on the ambition of next-gen autonomous vehicles because people would want a safe vehicle.
Accurate data annotation will come in handy here as it will help next-gen AV systems predict potential accidents and take preventive actions like braking or steering adjustments. This is essential for reducing collisions and accidents before they escalate. Here is how different data annotation techniques are used for this:
- Time Series Annotation: Tagging sensor data with timestamps to track how objects change over time (e.g., a vehicle accelerating too quickly or a pedestrian crossing the road).
- 3D Point Cloud Annotation: Labeling objects in 3D space using data from LiDAR sensors helps the vehicle understand the distance, size, and location of objects relative to itself.
- Road Sign Recognition for Smarter Driving
The AI models need to be trained to recognize speed limits, stop signs, warning signs, etc., so that they can adhere to traffic laws and adjust the vehicles accordingly. The following are some of the data annotation techniques used for the same:
- Polygon Annotation: Marks irregularly shaped signs like a circular sign of “No Parking” or “speed limit,” or a triangular warning sign.
- Semantic Segmentation: Understanding traffic signal colors (red, green, yellow) to ensure safe driving at intersections.
The Roadblocks in Data Annotation for Autonomous Vehicles
So far, we know the importance of data annotation for autonomous vehicle development, but it is not free from its share of challenges like:
- Data Diversity
AVs must be trained on diverse datasets (streets with varying traffic levels, multiple weather scenarios, and pedestrian activity) to analyze the road activities and conditions in real time accurately. Achieving this diversity is resource-intensive, as it requires extensive data collection from various touchpoints (e.g., collecting rainy weather road condition data from South-Asian countries) to ensure the vehicle can operate safely in all environments and at all places.
- Handling Edge Cases:
Data with unique road signs (like mountain warning signs), construction zones, and detours are difficult to annotate due to their low availability. While these edge cases are crucial for AV safety, their scarcity makes comprehensive annotation challenging and leaves gaps in the vehicle’s decision-making capabilities.
- Data Security and Privacy
Sensitive information, such as pedestrian faces and vehicle license plates, is frequently included in the training datasets for annotation. To stop this data from being misused, businesses must follow laws like the GDPR and put strong data protection procedures in place, like face blurring and number plate redaction. However, their ignorance of compliance requirements causes them to fail in this regard.
- Quality Control
Accurate model training requires maintaining high-quality annotations. However, businesses increasingly rely on automated annotation tools as the volume of datasets increases, which can lead to mistakes like incorrect boundary definitions and classification of complex scenarios. These errors may jeopardize the performance of the AV in the absence of strict quality control and human supervision.
- Cost and Time Constraints
Hiring subject matter experts (e.g., for labeling complex LiDAR data) and investing in annotation tools is necessary for data labeling for autonomous vehicles. Businesses with limited budgets often struggle with hiring and/or training employees and investing in tools and infrastructure. All of this ultimately causes delays in AV testing and launch schedules.
Due to these constraints, many companies are looking for options that can help them annotate data and meet delivery deadlines more effectively without being inefficient. One of the ideal approaches is outsourcing data annotation services.
Solving Data Annotation Challenges for AVs Through Outsourcing
Outsourcing data annotation can simplify the process of preparing datasets for autonomous vehicles (AVs). Here is how:
- Specialized Services: The majority of outsourcing partners offer all services (including text, video, and image annotation services) at one location.
- Subject Matter Expertise: To correctly categorize complex data, including LiDAR scans and pedestrian activity, service providers have expertise. As a result, companies can expect better model performance while meeting the particular needs of AV development.
- Cost-Efficiency: Service providers often bill on a project basis. This helps bring down the expenses related to recruiting, training, and sustaining in-house annotation teams for companies.
- Enhanced Data Security: Service providers hold ISO certifications, adhere to privacy laws like GDPR, and implement data protection measures to keep businesses out of legal issues.
- Humans-In-The-Loop (HITL) Approach: Service providers don’t just rely on automation. They integrate human oversight into the annotation process. Humans review and correct data labels as and when required during the whole process.
Conclusion
The next-generation autonomous cars will be one step ahead of the current AVs in comprehending complicated settings and making rational decisions while driving, but only if precise data annotation is done. Thus, manufacturers should invest in high-quality annotation tools, outsource image and video annotation services, and introduce the HITL approach.
Plus, make sure to keep up with evolving trends like the use of synthetic data, AR and VR annotation, & sensor fusion data annotation, as they will only scale in the future and improve the capability of AVs.