Data Annotation for Autonomous Vehicle Technology

The number of companies creating and innovating autonomous vehicle (AV) technology is growing. This is an important sign that there is a huge potential for the AV technology worldwide. The latest Autonomous Vehicles Readiness Index (AVRI) placed the US fourth place, behind Singapore and Norway, Singapore Netherlands as well as Norway. It was placed second in terms of innovation, after only Israel. India is in a very early stage, but has huge potential to develop the possibilities of AV technology. This is further substantiates the sheer increasing number of companies and startups developing AV technology around the world, particularly in countries such as the US.

AVRI is a scoring system developed by KPMG to assess a country's capability to integrate AVs on their roads. The AVRI scores countries on a variety of different factors, from infrastructure to innovation.

A number of countries are aware of the growing need and the potential of AV technology and are developing initiatives to support its development. For instance the US released an infrastructure bill worth $1 trillion which offers a variety of suggestions for modernizing infrastructure in order to facilitate the widespread use of AVs and mobility. But, both innovators and manufacturers must be able to master creating models that are able to function in any terrain.

The AV industry must be able to navigate through a dynamic and constantly changing environment, and must face a variety of unique issues, known as 'edge case scenarios'. Therefore, in addition to infrastructure, which is crucial for the successful operation of AVs innovation, the innovators should focus on quality dataset for machine learning and well applied AI (AI) models to anticipate and evaluate edge cases.

A greater focus is placed on safety of AVs.

There is a chance that you are wondering about the necessity of the use of AV technology. To understand why it is essential to examine the safety of vehicles risks and road safety violations across the globe. They've caused 1.35 million deaths as a result of accidents in the automobile up to 2018, with a person behind the driving the vehicle, according to the World Health Organization (WHO) which makes it one of the top causes of death among those aged between 5 and 29.

The challenge of reducing this figure is among the most pressing problems we'll have to face today in this century as transportation technology is at an record-setting high. In the world, car manufacturers as well as software companies have spent over $54 billion on the development of AV in the year 2019. Market research experts anticipate that this will grow by tenfold, reaching $556 billion in 2026.

It's not surprising that this industry has attracted numerous world top-tier leaders in AI software development, software development, and engineering of devices.

Data annotation and AV security

When we compare a vehicle operated by computers to the car driven by a person we're comparing viewpoints. As per the US' National Highway Traffic Safety Administration the country is hit by more than 6 million automobile crashes each year. There are more than 36,000 Americans suffer fatal injuries in these accidents while an additional 2.5 million being admitted to hospitals' emergency rooms. The numbers for the entire world are more shocking.

One may wonder whether a shift to AVs could reduce these numbers dramatically. However, those who are involved in various AV initiatives acknowledge that the most revealing aspect is the level of confidence among consumers. Are they willing to contemplate a fully autonomous vehicle or being transported through the streets in one?

A report from the year 2018 by the Rand Corporation report, 'Measuring Automated Vehicle Safety', examined the conflict between the requirement for data that is empirical for AV designers who want to push the boundaries of the art, and the desire of consumer safety regulators to resist what they consider to be a risk that is not avoidable. The report stated: "In the United States - and elsewhere, to some degree - the emergence of AVs has been associated at least implicitly with the view that some exposure to risk and uncertainty about this risk must be accepted in the short and medium terms to see the long-term benefit of AVs."

The importance of mapping

AVs must be more secure than vehicles driven by humans. That is the main objective. This can only be accomplished when all technologies employed can be integrated such as sensors, mapping as well as AI and predictive intelligence.

Mapping is crucial. The AV algorithm requires a mixture of roads, directions street images, traffic conditions as well as other directional factors to make sound decisions when processing the algorithm. All of this has to be performed to be done in real time. We expect A.V. technology to make predictions and make better decisions based on what's happening around the vehicle.

To collect valuable data at such a high level , requires sophisticated tools and technologies. For instance, Tesla counts on video-based systems, while the majority of AV manufacturers use LiDAR and video to collect the data they require. This is because it provides accuracy for location as well as depth perception.

LiDAR can be precise to within centimeters. It is able to make 3D maps for vehicles that have a sensors that span around 200 meters. LiDAR is not dependent on sunlight and is not susceptible to fog or rain because it uses near-infrared signals. Apple, Ford, Volkswagen, Microsoft, Hyundai and others are investing heavily into LiDAR. The research is turning to an arms race like many research it.

As a world leader in the field of ADAS annotation for autonomous vehicles and having added with more than 150 million data point we believe that data is crucial to allow AI models to function effectively. Because the development of autonomous vehicles is mostly a visual task, all training data is a type of video including still images and full-motion video. The cameras as well as other sensor in an AV system are bombarded with constantly changing streams of data. Certain are static, such as lampposts, fields, buildings and similar as the car drives along an avenue or highway and the rest are random situations that require immediate intervention from the car's AV computer , such as pedestrians flitting between the cars parked, a cyclist moving across or in the direction of the car, or another vehicle veering off into the same direction.

In each case the AV algorithm controlling the vehicle must make quick decisions regarding the character of the object as well as the threat it poses to the vehicle , or the reverse.

Data annotation types

Simply simply it can be described as the act of labeling or classifying objects captured within frames by using an AV. The data is then used to feed deep-learning models, and then labelled or tagged manually , or by using AI models or a mixture of both. This is essential in order to aid AVs to identify patterns in the data and categorize accurately in order to make the correct choice. It is equally important to use the correct kind of annotation in order to collect the most accurate data. These are the kinds of annotations that are suitable for AVs:

Bounding Box Annotation Notes on rectangular boxes to mark objects that are targeted

Semantic Segmentation: Notifying images after segregating into component parts

3D Cuboid Note: Utilizing 3D cuboids to depict desired objects, by using camera angle to create spatial-visual models

Keypoint/Landmark: Determining changes in shape by multiple points that are consecutive

Polygon Annotation: Annotating the object's edges precisely regardless of shape

Polyline Annotation: The Polyline annotation is used to mark lines that mark pedestrian crossings and double lanes, single lanes, etc., to help road users recognize roads.

3D Point Cloud Annotation: Annotating 3D point cloud annotations to aid in LiDAR and radar

Object Tracking: Tracking and locating objects within a set of pictures or point clouds in a sequence of frames

Instance Segment: The Instance Segmentation process involves identifying every instance of an object at every pixel in every object in an image

Panoptic Segmentation: Combining instances and semantic segmentation

Multi-Sensor Fusion: Combining LiDAR, Infrared and photos of multiple angles taken by various sensors

Edge cases

The advancement of the AV technology has been significant over the past 10 years. But, it is stymied by a huge obstacle which is hindering its widespread adoption. This obstacle is known as an edge scenario an unusual situation or unpredictability in which an AV is not able to properly address or recognize an unusual circumstance, obstacle or incident that occurs on the road, which could cause the death or injury of a person.

In comparison to a completely concentrated human driver AVs aren't able to detect and respond to any random event or development. To deal with edge situations, AVs require special design strategies to deal with these situations effectively.

Because ADAS data collection is usually produced manually for the purpose of instruction of AVS, it can be difficult to teach vehicles how to respond for unlikely incidents that might occur on the road. It's difficult for a computer system to comprehend the surroundings like a person.

The concept of environmental perception refers to a contextual perception of the environment or situation like the detection of obstacles, the recognition of markings or road signs, and separating data according to their semantic significance. A model that has been trained can anticipate several edges, and edge case training can help make your autonomous systems more durable in the face of new operational challenges.

For example the road sign that has the illustration of deer may be confusing when compared to an AV. The AV may view the sign as a thing and then stop abruptly. Someone with a trolley or pram on the road could not be recognized by the vehicle. It is essential to consider an appropriate balance between analysis and practical experience in the field because the edge case, just like cattle that are on the road can be one cause of failure if the person is not properly educated.

Every city, country and landscape have distinct challenges and edge cases, as well as training for the challenges they pose - a perpetual issue in and of for itself. Situation is an important factor when it comes to defining the character of any roadside improvement or sign. For instance, each country or city's police department has distinct style and appearance, and each country has its own distinct road regulations. While roadside cattle are common in certain areas of the world they may not be the norm in other areas.

The greatest security benefit for an anti-virus is the fact that it's not a person. It is made to obey all traffic laws and remain indestructible even by small things like flashing text messages and phones screens. They can also recognize what humans cannot, specifically edge cases, and can respond faster to avoid collisions, at the very least, in the theory of. A precise labeling of edge-case information helps to bring this theory into reality.

Edge cases can be a major barrier to the widespread acceptance, safety and effectiveness of AVs. The ability to anticipate and address these issues is a crucial element of the success of AVs. If you have the right technique, experience and knowledge of a specific region's unique challenges and challenges, they can be dealt with efficiently.

The future

The drive for AVs has brought about enormous innovations and driverless vehicles are on roads, changing the way we the way we travel. To sustain this rate of advancement, researchers will continue to require access to top-quality and affordable data. This is a great chance for experts in data annotation like us to work with process, people and technology to provide the most accurate data sets. To enable AVs to become a standard fact, data annotation companies and developers have to come up with new ways to address the issues that arise and develop data-driven systems that are reliable and insightful.

ADAS with GTS

To make this a reality, mechanization devices referenced prior in this blog can assist with accomplishing explanation at scale. Alongside this, you want a group that is sufficiently capable to empower information explanation at a huge scope. Are you considering outsourcing image dataset tasks? Global Technology Solutions is the right place to go for all your AI data gathering and annotation needs for your ML or AI models. We offer many quality dataset options, including Image Data Collection, Video Data Collection, Speech Data collection, and Text Data Collection.