The software and hardware that power autonomous vehicles are sophisticated, to say the least. By relying on complex machine learning algorithms like neural networks and artificial intelligence (Automotive AI), engineers have successfully paved the way for self-driving cars.
However, many people still don’t understand exactly how autonomous systems work. Common questions include: What ‘drives’ self-driving cars? Which devices are involved in an efficient autonomous system? How do machine learning algorithms work behind the scenes? What challenges remain for autonomous vehicles and how can we overcome them?
At Morey, we are advancing the development and adoption of emerging technology every day. It is our mission to bring smart, connected devices to life through creative design, meticulous manufacturing, and best-in-class products.
Read on to learn more about the fascinating dynamics of autonomous hardware, software, and networks.
Levels of Autonomy
According to SAE International, there are six levels of driving automation:
- Level 0 – Features are limited to providing warnings and brief assistance, but the driver must continue to control the vehicle.
- Level 1 – Provide steering OR brake or acceleration support. Examples of level 1 features include lane centering OR adaptive cruise control.
- Level 2 – While the driver is still in control of the vehicle, level 2 features provide both steering AND break or acceleration support. For instance, a car that supports lane centering and adaptive cruise control is considered an SAE level 2 vehicle.
- Level 3 – By progressing to level 3, the person is no longer driving the vehicle. However, when the features request, you must be ready to take over because they function under limited conditions.
- Level 4 – The next step to an autonomous vehicle includes all the features that no longer require the driver to take over at all. An example of this is a local driverless taxi, where pedals and a steering wheel may or may not be installed.
- Level 5 – Taking it one step further, a level 5 vehicle drives everywhere in all conditions.
Let’s dive into the tech: when you understand this, you’ll understand how the hardware and software applied to autonomous vehicles can take your business or fleet to the next level of safety and productivity.
4 Key Processes To Autonomy
Even though the word ‘autonomy’ is becoming more commonplace, there are specific processes that must be present for a system to be considered autonomous. Here are four of those and how they relate to autonomous vehicles:
- Perception – Perception tools use sensors to collect and process that data. When referring to autonomy devices in cars, perception involves everything that goes on around the vehicle and mimics a driver’s sense of sight.
- Localization – Localization tools identify the exact position of a vehicle while driving on the road in relation to various static objects. By pinpointing where a vehicle is at a specific moment, self-driving vehicles can move along a road to get to a destination.
- Planning – Local path planning tells a vehicle how to move across the road or any given environment, without hitting obstacles like trees, signs, and other cars. Motion planners produce a trajectory and velocity profile which is sent to an actuator, which in turn provides controls for basic functions like engine idle speed or valve timing.
- Control – The control system regulates and maintains the functionality of an autonomous vehicle by a few different factors. For example, control systems in self-driving cars command other parts of the car to perform effectively. These include the throttle, steering, and brake system.
By ensuring that an autonomous vehicle has each of these features, companies offer a viable alternative to person-controlled cars. This builds the foundation for different autonomy devices, which we’ll cover next.
Hardware
The following devices are crucial to self-driving technology:
- Stereoscopic cameras – These cameras have two or more lenses with separate image sensors or film frames. A stereoscopic camera simulates human binocular vision, allowing a driverless car to see in three dimensions.
- Radar – The two main types of radar include frequency-modulated continuous wave (FMCW) RADAR and Impulse RADAR. Because FMCW RADAR pulses are emitted continually and incorporate high-resolution range and depth perception, they are the more popular option for self-driving cars today.
- Sonar – Sound navigation and ranging can be passive or active, meaning these systems can either listen to sounds that come by the vehicle or emit sound pulses and read echoes. This allows an autonomous vehicle to detect other objects and communicate with them effectively.
- Infrared cameras – Thermal imaging cameras help autonomous vehicles navigate in suboptimal conditions, allowing level four and five vehicles to overcome shortcomings of other radar technology. For instance, infrared cameras aren’t affected by oncoming headlights, direct sunlight, or extreme lighting changes on the road.
- LiDAR – Light detection and ranging are some of the latest mapping technology for driverless vehicles. The four components that allow LiDAR to make decisions for a vehicle are a laser, receiver, scanner, and GPS.
- Electronic Stability Control (ESC) – This is an automatic system that keeps your car on course while steering. While a person drives a car, for the most part, ESC can assist the driver in serious conditions to maintain control. For instance, ESC can prevent a car from spinning out.
- Vision Cameras – Vision cameras may represent next gen tech for autonomous vehicles. They have many of the benefits of other technologies listed here. As of 2021, Tesla removed radar on Model 3 and Model Y vehicles. Instead, the “Tesla Vision” uses cameras, which are significantly more affordable. These vision cameras allow Tesla to equip all of its self-driving cars with the technology to make a vehicle fully autonomous.
- Global Navigation Satellite System (GNSS) – An accurate localization solution for autonomous vehicles, GNSS technology ensures that a vehicle stays in its lane and remains at a safe distance from other cars on the road.
Software
With the right software, a car is transformed into an intelligent machine, capable of improving safety and efficiency.
The main algorithm behind self-driving cars is a convolutional neural network (CNN). Below, we’ll review how Tesla, Waymo, and Nvidia use CNN in their autonomous vehicles.
- HydraNet by Tesla – HydraNet was developed by Ravi et al. (2018). Basically, the software has different CNN networks that are assigned to specific tasks. When classifying an image, HydraNet uses a gating function to determine which components should activate to complete each subtask, decreasing computing power while maintaining a high level of accuracy.
- ChauffeurNet by Google Waymo – This is an RNN-based neural network, but CNN is still a core component of the software. ChauffeurNet is a convolutional feature network, also known as a FeatureNet. The conceptual basis is imitation learning, which involves mimicking human behavior for a given task. Something that sets ChauffeurNet apart from the typical self-driving car software is that the perception system is a mid-level system, meaning there are different levels of input coming from the perception system.
- Nvidia Self-Driving Car – Nvidia also uses CNN for its self-driving car. However, this minimalistic system uses a smaller network to solve problems with fewer resources through self-optimization. A few use cases for the Nvidia self-driving car include navigating roads without lane markings and detecting useful road features.
Behind the Scenes: Machine Learning Algorithms
Before we can enter the world of data science, it’s important to understand how all the forms of data come together in self-driving cars. The answer is Multi-sensor fusion, which involves taking inputs from RADAR, LiDAR, camera, and ultrasonic sensors and putting them together to understand environmental conditions and make decisions.
Once that data is combined, innovators harness the power of machine learning algorithms, including convoluted neural networks and imitation learning. There are a few other machine learning algorithms that play a key role in self-driving cars. These include:
- Reinforcement Learning – The car learns by investigating and interacting with the environment. When a vehicle needs to choose a specific action to take in a given scenario, it utilizes deep reinforcement learning (DRL). The three variables of DRL are the state, the action, and the reward. The state is the current position on the road; the action includes all the possible things the car can do, and the reward is the feedback after taking an action.
- Edge Computing – Computing storage and managing and analyzing data. The main benefit of edge computing is the ability to process data in real time, and in turn, the vehicle can immediately react to changing conditions.
- Fog Computing – This is a branch of cloud computing that reduces the traffic volume sent to the cloud server by pre-computing data. In addition, fog computing uses vehicle-to-vehicle (V2V) communication, as well as between-a-network communication.
Networks for Autonomous Vehicles
Depending on the type of communication needed in a self-driving car, various networks are available. For instance, in vehicle-to-vehicle communication, the most basic type of communication, information about one car on the road is conveyed to another to avoid accidents. Speed and position data are transferred to allow for a self-driving car to adapt to the current road conditions.
On the other hand, in vehicle-to-everything (V2X), the car incorporates a high-bandwidth, low latency, and highly reliable form of communication. They feature all different types of transport-related applications, such as cars communicating with one another as well as parking spaces and pedestrians.
One question that comes up is ‘Do I need 5G for a self-driving vehicle?’ The answer isn’t concrete but it’s clear that LTE and 5G networks are key components of self-driving cars today, which offer higher quality services within an autonomous vehicle.
Autonomous Systems: The Verdict
Autonomous vehicles are becoming more and more common, but do we truly understand how they work? Hopefully, after reading this article, you’ve developed a basic understanding of autonomous systems, hardware, software, and networks. You can imagine the opportunities and applications for fleet telematics and the team at Morey is already leveraging emerging technology like this in strategic ways.
From the four basic principles of autonomy to the various devices that make it possible and the machine learning algorithms operating behind the scenes, self-driving cars are inherently complex. The key to making the most of all we’ve gained from working on this technology is to adapt to changes and apply our enhanced understanding, considering how these advances can be put to work to support efficiency, security, cost-savings, and more.
If you’re ready to explore a partnership with the team of innovators at Morey, schedule a call anytime.
Want to see principles like these in action? Check out our case studies for a better understanding of how we’ve helped major companies such as Verizon and Panasonic improve reliability and increase their ROI with us as their IoT partner.