Self-Driving Cars: A Spotlight on Artificial Intelligence (AI)
The market size for AI-driven automotive solutions is expected to grow more than tenfold by 2025, increasing the business opportunity area of the in-car experience and the importance of unbiased training data for AI models. In this article, we will introduce the key components of the out-of-car experience and the main content of automatic driving data annotation.
The development path of autonomous driving
When it comes to out-of-car experiences, the focus remains on self-driving cars. While the goal of the effort is to reach the highest level of fully automated driving (Level 5), until that point is reached, how AI will impact the experience outside the car will be a gradual process. AI-driven smart cars require higher levels of computer vision and computing power—sensors for radar and cameras transmit massive amounts of data every second to deal with things like dangerous road conditions, roadblocks and road signs.
Data labeling for autonomous driving
Self-driving cars not only need to understand the occupants in the car, but also be able to find the right way to deal with complex road conditions. This is a safety-first AI application scenario where there is little room for error. While full automation is a long road, moving slowly will also help build trust with consumers as automakers move toward driverless levels. The business opportunity for AI-driven autonomous driving is centered around computer vision with LiDar , video object tracking, and sensor data, thanks to the latest research in machine learning models for computer vision. These data-labeling services that help cars "see" and "think" while driving from point A to point B, and help train models to perform actions include:
Point Cloud Annotation ( LiDar , Radar)
Understand the scene in front of and around the car by identifying and tracking objects in the scene. Merge point cloud data and video streams into the scene to be labeled. Point cloud data helps your model understand the world around the car.
2D annotation (including semantic segmentation)
Help your models accurately understand information from visible light cameras. Look for data partners who can help create scalable bounding boxes or high-resolution pixel masks for custom ontologies.
Video Object and Event Tracking
Your model must understand how objects move over time, and your data partner should assist you in annotating temporal events. Track objects (such as other cars and pedestrians) entering and leaving regions of interest in the ontology in multi-frame video and LiDar scenes. It is critical to maintain a consistent understanding of an object's properties throughout the video, regardless of how often the object enters and exits view. In order to ensure driving safety, the requirements for autonomous driving training data are very strict: high quality, high volume, and high efficiency. In the labeling process, the artificial intelligence-assisted labeling platform plays a core role: it can perform artificial intelligence pre-labeling, greatly reducing labor time consumption, and can perform high-quality and fast quality inspection, which cannot be achieved by pure manual labeling. For example, Appen's high-quality data labeling platform has been fully upgraded for automatic driving data labeling. The 3D point cloud lane line semantic segmentation automatic recognition capability constructed in it can complete the lane line point identification with dozens of times higher efficiency than manual labeling. Classification.
Most Important: Find a Trusted Data Partner
As both in-vehicle and out-of-vehicle experiences are directly related to manufacturer KPIs and focused on consumers, the time is ripe for AI adoption and scalable deployment in both areas. However, any deployment scheme is difficult to achieve without training data support. Traditionally, automakers have had to rely on multiple vendors and applications to collect, prepare and integrate all the data in order to effectively train their AI models. But now, whether you want to build a level 1 or level 5 autonomous driving solution, improve driver assistance functions, or build a solution in between, Appen can provide a unified product, using a comprehensive Automated data flow to help you complete the intelligent construction of vehicles from vehicle intelligent systems to autonomous driving. With our diverse datasets covering common and rare usage scenarios, and the team of experts needed from training data preparation to deployment, we help you confidently achieve the highly accurate AI deployments required in the smart-driving car space.