Unlocking the Power of AI: Demystifying the Importance of Training Datasets

Why is ADAS sensor data collection crucial to the development of self-driving cars?

What is ADAS?

Firstly, what does this strange acronym stand for? ADAS means advanced driver assistance systems. It is a generic denomination to designate all past, current, and future electronically-controlled, advanced safety systems in a car. It includes, but is not limited to, automated emergency braking systems (AEBS), lane-keeping assistance systems (LKA), adaptive cruise control (ACC), blind-spot monitoring (BSM)… Yes, the world of ADAS is full of acronyms!

 

But behind the capital letters hides essential technology that saves lives. Over the past decades, these systems have been gradually introduced to enhance safety in vehicles. Based on the information captured by the sensors around the car, the system will warn the driver of a critical driving condition. The lane-keeping assistance system, for example, uses front cameras to detect the presence and trajectory of the driving lanes. If the driver veers unexpectedly off the lane, the system issues sensory warnings to the driver, with a short beep, a warning light, and a sensation of resistance in the steering wheel.  In some situations, depending on the level of automation of the car, the assistance system might even initiate – or execute – the braking or escaping maneuver.

But why do you need to collect ADAS sensor data?

At first, the technology may seem relatively straightforward. Various sensors, short and long-range radars, low or high-resolution cameras, or even lidars (3-D laser scanning sensors), gather the information. The car’s electronic control unit (ECU) uses this information to meet an appropriate driving decision, such as issuing a warning, slowing down, or applying the brakes. But real-life driving conditions are rarely simple ones.

 

Take the example of the pedestrian detection systems. These systems are designed to avoid a potentially fatal collision between cars and pedestrians on the roads. Yet most systems aren’t nearly as efficient as they should be. The American Automotive Association performed a study in October 2019. The association warns vehicle owners that they should not blindly rely on their collision avoidance systems and remain engaged drivers. While most systems detected the presence of an adult crossing the streets, a vast majority failed to avoid a collision with a child darting out. Worse, all systems failed to properly detect pedestrians at night.

A child catching a ball in the middle of a street is detected by the ADAS data collection used in sensor of a car.

ADAS sensor data helps validate the pedestrian detection systems.

Testing and validating the systems using real-life data is a must. Thinking about it, how does a pedestrian look like? How tall is he, what garment does he wear, how fast does he move? An anti-collision system must be able to identify any pedestrian crossing the road, adults and children alike, even a person in a wheelchair. Equally, the system should not apply the brakes when spotting the image of a running child featured on a digital billboard. It must be smart enough to identify real driving hazards and set them apart from non-threatening situations.

 

What vehicle manufacturers need to train the algorithm of the ECU is data. Information about the myriad of possible driving situations that the ADAS system has to consider. Only then it is possible to train the algorithm to make the right driving decision in any situation.

What is the procedure for ADAS function?

Many late-model vehicles include ADAS in their initial design. These systems are then updated as automakers introduce new models, and add more features. They make use of numerous dataset for machine learning inputs in order to offer important security features. A few of these sources include automotive imaging which is a set of high-quality systems that are able to mimic and surpass those of the eye with regards to 360-degree coverage and 3D object resolution. excellent visibility in adverse conditions of lighting and weather, and real-time information.

LiDAR (light detection and moving) includes more sensors and cameras for computer vision. It transforms outputs into 3D with the ability to distinguish between moving and static objects, allowing for additional layers of bad-lighting or blind spots.

Additional inputs can be obtained from other sources not part of the primary vehicle platform, including other vehicles (V2V) or vehicle-to-Infrastructure (V2X) — Wi-Fi, for example. The next-generation ADAS continues to utilize wireless networks to offer more security and value through the use of V2V and V2X-related data.

Why is ADAS crucial?

Most road accidents are the result of human errors. These advanced safety technologies were designed to improve and automatize aspects of driving to improve safety and secure driving practices. ADAS annotation have been shown to lower the amount of road fatalities as well as reducing the likelihood of human errors.

The technologies are divided into two broad categories one of which is ones that automatize driving, such as automated emergency brake systems, and ones that assist in improving the drivers’ awareness like warning systems for lane departures.

The main purpose of these security systems is to enhance the safety of drivers, while also reducing injuries through reducing the total amount in traffic crashes. They also reduce the amount of insurance claims that result from minor accidents where there is damage to property but there are no injuries.

ADAS conclusion

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.