The Role Of Machine Learning In Autonomous Vehicles
Created on August 22|Last edited on August 26
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Introduction
We’re still in the early days of autonomous driving, but the technology is advancing rapidly. The goal is to have vehicles that can drive themselves safely and efficiently, without the need for a human driver.
There are many potential benefits to autonomous driving, including improved safety, efficiency and convenience. For example, self-driving cars could help to reduce traffic accidents, congestion and emissions.
The technology is still being developed and refined, but there are already a number of companies testing autonomous vehicles on public roads. We’re likely to see more and more of these vehicles on the roads in the coming years, as the technology continues to improve.
Superpower Machine Learning for Autonomous Vehicles with Weights & Biases
Being the single-system of record for all of your machine learning work and research, Weights & Biases can be used to in a number of machine learning and data engineering tasks in AVs:
- Track your machine learning experiments when training models
- Version your AVs datasets
- Optimize hyperparameters for your models
- Visualize and query tabular data
- Collaboratively share findings and work on discovering better models and architectures
Companies working on Autonomous Driving
There are many companies doing great work in the autonomous vehicles space, here are just ten of them that I would like to highlight.
Tesla
The company that probably needs no introduction when we are talking about autonomous vehicles, as it's been making a lot of news with both successes and shortcomings of their autonomous systems.
Tesla is working on an autonomous driving system that will allow its cars to drive themselves, The Autopilot. Tesla's autopilot system - in terms of what's available on the road now - is still far from full self-driving as the drivers are required to keep their hands on the wheel and to be paying attention to the road.
Arguably, Tesla has some of the most advanced AV systems that are currently operational on public roads.
Rivian
Rivian is an American automotive and energy company founded in 2009. The company's mission is to develop products and services that inspire people to live sustainably. Rivian is known for its work on electric vehicles and autonomous technology. Rivian has also been working on autonomous vehicle technology and has partnered with Amazon to develop a fleet of self-driving vehicles.
Zoox
Zoox is a company that is developing autonomous vehicles which will be used for public transportation. The company has been working on its technology for several years and has now raised over $800 million in funding.
Zoox has a unique approach to autonomous vehicles, and its vehicles are designed to be fully autonomous from the start. The company is also working on a new type of battery which it claims will be much cheaper and more efficient than current batteries.
Wayve
Wayve AI is a London-based technology startup that designs and develops artificial intelligence systems for autonomous vehicles. The company's technology allows vehicles to learn how to drive without human intervention.
The company was founded by Alex Kendall, Andrew Davison, and Daniel Polani in 2017. Wayve AI is a member of the UK Autonomous Vehicle Initiative, and is backed by number of investors.
General Motors
General Motors is one of the world's largest automakers. The company is developing self-driving cars and has invested heavily in the technology. GM's self-driving cars are equipped with sensors and software that allow them to navigate without human input. The company is testing its self-driving cars in a number of cities across the United States. GM also owns an 80% stake in Cruise, one of the more well-known self-driving car companies out there.
Lyft
Lyft is one of the leading companies in the development of self-driving vehicles, and has been working on self-driving for a while now. The company is currently testing its self-driving vehicles in a number of cities across the United States. The company believes that self-driving cars will make transportation more efficient and affordable.
Uber
As self-driving vehicles become more prevalent, companies like Uber are preparing for a future in which autonomous cars are the norm. Uber has been testing self-driving cars in Pittsburgh and Phoenix, and the company has plans to roll out a fleet of autonomous vehicles in San Francisco.
While the technology is still in its early stages, Uber is betting that self-driving cars will eventually transform the ridesharing landscape.
Waymo
Waymo is a self-driving technology development company that is a subsidiary of Alphabet Inc. The company started as the Google's self-driving car project in 2009, and has since been working on developing autonomous driving technology with the aim of commercializing it for use in personal vehicles, ride-sharing services, and logistics.
Ford
Ford as an automaker needs no introduction, and the company has also been working on autonomous vehicles.
Ford is investing around $7 billion in self-driving technology over the next 10 years. The company has been testing autonomous vehicles in various cities across the United States in partnership with Argo AI. From 2021 onwards, Ford plans to invest $5 billion of the total $7 billion budget into autonomous vehicles.
Motional
Motional is an autonomous vehicle company that was founded in 2020 as a joint venture between Hyundai Motor Group and Aptiv. The company is headquartered in Boston, but also has locations in Pittsburgh, Singapore, Las Vegas, and Los Angeles. In February 2021, Motional began testing its newest generation of vehicles in Las Vegas. The company also operates vehicles in Pittsburgh and Santa Monica, California.
The Levels of Autonomous Vehicles
There are 6 levels of autonomous vehicles, ranking from level 0 being no automation, to level 5 having full automation.
This is important to understand because a company trying to reach a given level of autonomy will have to solve very different problems depending on whether it's going for Level 1 or Level 5 autonomy.
The Levels Of Autonomous Vehicle Development Are
Level 0: No automation. The driver is in full control of the vehicle.
Example: Traditional cars.
Level 1: Function-specific automation. The vehicle has some automation features, but the driver is still in control.
Example: Adaptive cruise control.
Level 2: Combined function automation. The vehicle has multiple automation features, but the driver is still required to pay attention to the road.
Example: Tesla's Autopilot feature.
Level 3: Limited self-driving automation. The vehicle can handle most aspects of driving itself, but the driver must be ready to take over at any time.
Example: Audi's Traffic Jam Pilot prototype.
Level 4: The vehicle has nearly full autonomous capabilities, but there are still certain circumstances under which the driver must take control.
Example: Level 4 autonomous vehicles are not yet commercially available, but there are a few prototypes, such as the Google Self-Driving Car.
Level 5: Full self-driving automation with no human driver required. The vehicle can handle all aspects of driving itself and does not require a human driver.
Example: There are no Level 5 autonomous vehicles commercially available yet, but that's the ultimate goal of many companies, including Tesla.
The Many Tasks Of Machine Learning For Autonomous Vehicles
As the development of autonomous vehicles continues, the need for machine learning grows. AVs must be able to perform many tasks in order to be fully autonomous, including but not limited to: detecting and avoiding obstacles, following traffic rules, and making decisions based on changing conditions. Each of these tasks presents a unique challenge that must be addressed by an ML system.
In order to build an AV that can complete all of these tasks, engineers must first build a system that can interconnect and inter-understand all of the different ML systems. This is no easy feat, as each system is designed for a specific task and may use different data formats and standards. However, it is essential in order to create a fully autonomous vehicle.
This big number tasks involving ML that need to interconnected presents a unique challenge. While each task is important on its own, the real challenge lies in building a system that can understand and use data from all of the different tasks. This is essential in order to create a fully autonomous vehicle.
Vehicle Localization
Vehicle Localization is the autonomous vehicle task whereby the location of the vehicle is identified within a known environment. This process is achieved through the use of sensors and mapping information to determine the position of the vehicle. The accuracy of vehicle localization is essential for safe and efficient operation of autonomous vehicles.
Pedestrian Detection
As a a driver, you don't only encounter cars on the read, so you need to be able to perform pedestrian detection, which is a task that is responsible for detecting pedestrians in the environment and predicting their future trajectory in order to avoid collisions. This is a difficult task due to the many different possible scenarios that can occur, such as pedestrians crossing the street or walking in a crowd.
Traffic Sign Detection
This is a task that is crucial for the safe navigation of the vehicle. Traffic signs provide important information to the autonomous vehicle about the local traffic rules and regulations. In order to detect traffic signs, the autonomous vehicle needs to be equipped with a suitable sensor, such as a camera, and a robust traffic sign detection algorithm.
As with most tasks concerning advanced recognition of objects in images (which traffic signs are) applications of convolutional neural networks are leading the way.
Road-marking detection
Detection of road-markings in AVs is a task that involves finding and classifying markings on the road surface in order to provide information to the vehicle’s control system.
Road markings are used to indicate lane boundaries, traffic signs, and other information to drivers. They can be made of paint, reflectors, or other materials, and are often visible even at night or in bad weather.
Road-marking detection is a challenging task for autonomous vehicles, as the markings can be faded, obscured by dirt or debris, or simply not well-defined. However, it is a vital safety feature that can help the vehicle avoid accidents and to drive safely.
Automated Parking
So, this is a task allowing vehicles to park themselves without human intervention. In an autonomous parking system, the vehicle is equipped with sensors and software that enable it to identify a parking spot and park itself. The system may also include a camera to detect obstacles and a GPS system to navigate to the parking spot.
One advantage of automated parking is that it can reduce the amount of time it takes to park a vehicle. In addition, it can also improve safety by reducing the chance of human error.
Self-localization
In AV's, self-localization allows for estimation of the vehicle's pose within an environment from sensory data. This information can be used by the vehicle to navigate through the environment without the need for external localization systems, such as GPS. Self-localization is a critical part of an autonomous vehicle's ability to safely and efficiently navigate its environment.
There are many different methods that can be used for self-localization, such as:
- Lidar
- Inertial Navigation
- Visual Odometry
- Radar
Lane Detection
Probably one of the most well-known tasks that in autonomous vehicles. It's task of detecting the lane markings on the road surface and estimating the lane boundaries. This is a key task of autonomous driving, as it enables the vehicle to localize itself on the road and navigate safely. Also, the lane markings can provide valuable information about the road geometry, such as the curvature, and can be used to estimate the vehicle's position relative to the center of the lane.
There are many challenges in lane detection, such as occlusions, shadows, and varying widths of lane markings. Recently, deep learning has been shown to be most effective in tackling these challenges.
Driver Assistance Systems
In autonomous vehicles, driver assistance systems use sensors and mapping data to identify features on the road and navigate the vehicle accordingly, and to automate improve or adapt some or all of the tasks involved in operating a vehicle.
These systems are designed to operate in a wide range of conditions, including poor weather and lighting conditions. One of the challenges in designing these systems is that the data from the sensors is often noisy, making it difficult to accurately identify features on the road.
Vehicle Cybersecurity
As you can imagine, when we're talking about Level 1, 2, and going forward levels of autonomy: more and more aspects of driving a car are being controlled by someone other than the driver. And, so long as the AV systems are working well, it shouldn't be a problem. However, as you can imagine that if someone with malicious intents would hack into such a car, the consequences can be deadly.
So, vehicle cybersecurity is crucial and involves the process of making sure that the electronic systems in a vehicle are secure from malicious cyberattacks. This includes the electronic systems that control the vehicle's engine, brakes, and steering, as well as the systems that manage the vehicle's onboard computer, infotainment system, and other electronic devices.
There are a number of steps that can be taken to improve vehicle cybersecurity, including installing software updates and patches, using security devices and systems, and training employees of AV companies on cybersecurity best practices.
The Advantages Of Autonomous Vehicles
Of course, the benefits will depend on the levels of autonomy, but even the most basic techniques such as keeping the car within lanes can greatly improve the driving experience.
If we are talking about higher levels of autonomy, I would like to highlight the following benefits:
Safety
There are many advantages to autonomous vehicles. One of the most obvious advantages is safety. With autonomous vehicles, there is the potential to drastically reduce the number of accidents and fatalities on our roads.
In 2021, there were over six million car accidents in the United States alone, resulting in nearly 42,915 fatalities and 2.3 million injuries. The vast majority of these accidents were caused by human error, such as distracted driving, drunk driving, or simply poor driving. Autonomous vehicles (that are engineered well) are not subject to these same risks, and could potentially reduce the number of accidents and fatalities on the road each year.
Increased Efficiency
In addition to safety, autonomous vehicles can also help to reduce traffic congestion and pollution. By being more efficient, autonomous vehicles can help to reduce the amount of time and fuel wasted sitting in traffic.
Autonomous vehicles could potentially help to reduce or eliminate traffic jams by communicating with each other and coordinating their movements.
And, because they can be built lighter (due to a lower risk of accidents), autonomous vehicles can also help to reduce the impact on the environment.
Greater Accessibility
Another potential advantage of autonomous vehicles is increased accessibility. For example, elderly or disabled individuals who are unable to drive may be able to use autonomous vehicles to maintain their independence.
Improved Quality of Life
Personally, I also believe that autonomous vehicles have the potential to greatly improve our quality of life. Imagine being able to get 9 or 10 hours of driving done while catching a movie and sleeping through the night in the bed that’s replaced your seats. Or, imagine being able to get a quick workout in on a rowing machine during your commute to work. With autonomous vehicles, all of this is possible.
Conclusions
The role of machine learning in autonomous vehicles is to enable the vehicle to learn from data and make predictions about the world around it. Machine learning algorithms can be used to identify objects, pedestrians, and other vehicles on the road, and to predict their behavior. This information can be used to make decisions about when to brake, turn, or accelerate, and how to steer the car in general.
All of this can help bring the time when we are able to truly enjoy the benefits of the age of self-driving cars. However, even not fully-autonomous systems (e.g. Level 1, Level 2, Level 3) can greatly improve the driving experience and so it's important to be working on those problems as well.
And who knows, maybe in a number of years it'll be feasible to be the only person in the car that's driving itself and read an article like that in it.
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