Determining The New Gold Standard of Autonomous Driving

By Abhilash Malluru
Feb 27, 2023

Autonomous driving is on the cusp of widespread adoption. As more manufacturers across the globe begin implementing AD systems in their vehicles, it is only a matter of time before it becomes a regular feature in future automobiles. And with the rise in popularity of AD systems comes a need for standardization.

Emerging standards are beginning to regulate how manufacturers approach navigation, safety, and AD modeling quality. These standards also influence policy creation, technology use, and the general framework for AD systems. Creating standard systems for these AD models will lead to a more uniform approach toward autonomous driving models.

An Overview of the Tech Behind Autonomous Driving

While the idea of autonomous driving dates back centuries with Leonardo da Vinci's inventions, most of the tech has been developed in the last few decades. After Navlab5's self-steering vehicle made headlines in the '90s, autonomous driving really took off.

The first AD production vehicle started with Tesla's Autopilot, an SAE 1 implementation that offered parking assistance and automated driver-assistive processes. Tesla doesn't provide a fully autonomous platform for their production vehicles, but the Autopilot helped gauge interest in the general public.

Other manufacturers are also spearheading their own development of AD vehicles. For example, Volvo's recent acquisition of Zenseact, a leading software and hardware developer for autonomous driving, shows the company's commitment to producing a fully autonomous vehicle. Volvo has also started implementing more sophisticated technologies like LiDAR for its AD driving platforms.

LiDAR and other data annotation methods - like bounding boxes, polygons, and key points - have become ubiquitous in the autonomous driving space. These annotation methods rely on trained AI models with massive data sets that provide accurate information to the vehicle in real time so it can adapt and adjust to conditions on the road.

It's extremely time-consuming to develop models, so there are still limitations, like a reliance on the driver to make crucial driving decisions. Still, this progress is leaps and bounds from where the earlier assistive processes were just a few years ago.

State governments in the United States have already convened and passed legislation regarding autonomous vehicles on public roadways. The most noteworthy is California, which has the most comprehensive regulations for autonomous vehicles. No federal legislature permits the deployment of fully autonomous vehicles yet. It operates more on a state-by-state basis.

The Standards Fueling AD's Mass Adoption

Common methods and standards have grown around the autonomous driving industry. Some of these are just general classifications, and others go down to how the vehicles actually function. As the market around AD grows, it only makes sense that there are more robust systems taking hold to define how these vehicles should safely and effectively operate.

SAE and IEEE

SAE and IEEE have convened and already passed their own guidelines defining what autonomous vehicles are and how to classify them. IEEE has more exhaustive standards regarding safety on public roadways and connectivity between other cars. These aren't necessarily driving the actual development behind Autonomous Driving. But they show that AD has reached a somewhat wide-scale acceptance among the various bodies developing the hardware and software that fuels it.

Simulations

Simulation is a vital method for developing and testing autonomous driving technology, enabling engineers and researchers to create a virtual environment that mirrors real-world conditions without putting people or property at risk. Simulation offers several benefits to developers, including cost-effectiveness, replicability, safety, scalability, and flexibility.

The cost of building and testing a physical vehicle can be high, but simulation can reduce expenses significantly. Simulating various driving scenarios in a virtual environment can help developers identify potential problems and make necessary adjustments without requiring physical testing, saving both time and money.

Simulations are highly replicable, meaning that a particular scenario can be repeated many times to test different algorithms, sensor configurations, or other variables. This enables developers to gather large amounts of data and draw reliable conclusions from their experiments, providing the necessary information to create efficient autonomous driving systems.

Simulation offers safety benefits as well. As autonomous driving technology is still in its early stages, testing in the real world can be risky. Simulating scenarios allows developers to test their technology in a safe environment, reducing the risk of accidents or injury.

Scalability is another benefit of simulation, as it can handle large amounts of data, allowing developers to test various algorithms and scenarios at the same time, while flexibility enables quick modification of variables and testing of different scenarios, reducing the time it takes to identify and address potential issues.

Vision Performance Standards

Much like the human driver behind the wheel, an autonomous vehicle needs a constant feed of visual data to interpret its environs. Visual performance is a crucial component behind autonomous driving and enables the car to recognize objects and react appropriately to them on the roadways. There are a few emerging standards empowering this innovation. For example, Intersection over Union (IoU), Average Precision (AP), and Mean Average Precision provide guidelines for visual processing implementation.

AP and IoU function similarly, dictating the visual detection system's accuracy in predicting the movement of detected objects. Mean Average Precision can work like AP, but it looks at numerous data sets to effectively process visual detection.

System Implementation Standards

LiDAR is one of the many standard systems emerging behind autonomous vehicles. Beyond just bare visual processing and prediction, LiDAR helps accurately map a car's surrounding environment. It isn't intended for the predictive positioning of objects necessarily but provides a quicker and more accurate image using light. Think of it as a more refined and advanced take on the role radar has served in assistive technologies.

Radar in vehicles has been a cornerstone for autonomous driving for a few years. It has helped inform collision detection, lane keeping, and blind spot awareness. Plus, radar works with robust visual imaging suites and LiDAR for complete awareness of everything around the vehicle.

NHTSA

The National Highway Traffic Safety Administration is making real headway toward providing guidelines about what AD needs to be truly ready for America's roads. The NHTSA has done quite a bit in standardizing automobile safety features over the past few years and made 2016-2025 safety feature stipulations for auto manufacturers. These recent additions are partially automated and very much in line with the aims and goals of autonomous driving. They also include items like lane-keeping assists, adaptive cruise control, and traffic jam assists. NHTSA has a stated goal for all new automobiles manufactured in the United States to have fully automated safety features from 2025 onward. With the headways made in the aforementioned systems, they very well may be on their way to ushering in autonomous driving across a wide swathe of vehicles.

Moving Forward With Autonomous Driving

Autonomous driving has progressed significantly toward providing standardized systems and guidelines for developing autonomous vehicles. As these vehicles - and their technology - mature, there will only be more robust frameworks and guidelines to bolster them.

Are you looking to integrate actionable experience towards developing your own autonomous driving systems? Digital Divide Data has the means and experience to develop robust systems adhering to the guidelines mentioned in this article. We offer support for a wide variety of visual imaging, object classification, and semantic segmentation. If you're looking to bolster your AD platform, choose DDD to supply industry know-how for your data annotation.