With the growing convenience of accessing information at our fingertips, users desire similar seamless experiences even in vehicles, leading automakers to develop driver assistance systems.
Driver assistance systems come in two types – Passive ADAS and Active ADAS. The former emits auditory and visual signals to alert the driver, while the latter prevents accidents by taking control of the vehicle without human intervention. The effectiveness of driver assistance systems relies on a combination of technologies designed to assist drivers in making decisions.
The World Health Organization (WHO) estimates that motor vehicle crashes result in approximately 3000 fatalities every day. A significant portion of these accidents is due to driver error. An analysis of crashes in the US between 2005 and 2007 identified distractions, sleepiness, and excessive speed as major contributing factors. Due to the increasing need to assist drivers, the demand for driver monitoring systems is surging.
The Power of In-Cabin Data
Creating autonomous vehicles starts by acknowledging the crucial role of in-cabin data. Whether in self-driving cars or piloting autonomous aircraft, passengers and drivers generate essential information, including user preferences and behavioral patterns. This data forms the foundation for building safer and more efficient autonomous vehicles.
In-cabin monitoring or sensing utilizes cameras and microphones to track and analyze driver and occupant activities within a self-driving vehicle. This technology can identify expressions and emotions through cameras, while microphones capture conversations to gain further insights into their state. Here are a few things that the system can track:
Driver and Occupant Monitoring
- Track the driver’s attention and monitor eyelid movements, head position, or other impairments for signs of distraction that could affect driving safety.
- Track occupants’ behavior and identify if any passengers are vulnerable, such as children in the child seats or even sticking hands or bodies outside the car window.
Behavioral Analysis
- Recognize the emotional state of drivers and occupants to identify stress, fatigue, drowsiness, frustration, or contentment.
- Identify driver and occupant behavior like using mobile devices, eating, or other activities.
Safety and Security
- Identify safety-related issues such as unbuckled seat belts or possessing illegal or dangerous items within the vehicle.
- Detect theft of intrusion into the vehicle.
- Advanced driver monitoring systems (DMS) equipped with in-cabin sensing technology automatically identify vital signs and medical emergencies.
Data Annotation Types for In-cabin Monitoring
Data annotation plays a crucial role in in-cabin monitoring, contributing significantly to the accuracy and effectiveness of these systems within autonomous vehicles. Here are some key types of data annotation relevant to in-cabin monitoring:
Occupant Identification
Bounding boxes denote the position and boundaries of individuals within the cabin, allowing the system to identify and track occupants.
Facial Expressions
Key points help identify and analyze facial expressions, and emotion labels annotate specific emotional states (e.g., happy, sad, surprised) with corresponding facial expressions.
Activity Recognition
Temporal Segmentation helps mark the time intervals during which specific activities occur within the cabin, and activity labels signify different activities, such as talking, eating, reading, or using electronic devices.
Object Recognition
Annotating objects within the cabin, such as bags, electronic devices, or others, helps recognize and classify objects to enhance understanding of the in-cabin environment.
Anomaly Detection
It involves marking abnormal behavior or situations for training models to detect potential safety risks.
Attention Labeling
It includes identifying areas within the cabin that occupants focus on, contributing to a better understanding of their interests or concerns.
These data annotation types and many others collectively contribute to training robust machine learning models for in-cabin monitoring, enabling autonomous vehicles to respond intelligently to occupant behavior and ensuring a safe, comfortable, and secure environment.
In-cabin Monitoring Data Annotation Solution by iMerit
Data annotation solution for in-cabin monitoring by iMerit, purpose-built for Driver and Occupant Monitoring Systems (DMS), ensures the safety and security of drivers, passengers, and vehicles.
Level 3 autonomous vehicles rely on high-quality annotated data for performance and safety validation. It is crucial for meeting regulations for future advancements. iMerit leverages the Ango Hub platform to provide in-cabin sensing data labeling solutions, combining AI models for automation with human expertise to identify edge cases and potentially dangerous situations. With the Ango Hub platform, it becomes easy to create and manage customized data labeling workflows, helping define the sequence of various steps in data annotation.
Learn more about our in-cabin monitoring data annotation solution.
