The guide to understanding the state of the art in hardware & software for self-driving vehicles.
Summary
At the start of the 2020s, the state of autonomous vehicles is such that they have achieved the ability to drive without human supervision and interference, albeit under strictly defined conditions. This so-called level 4, or high automation, has been reached among many unforeseen challenges for technology developers and scaled back projections.
No technology is yet capable of Level 5, full automation, and some experts claim this level will never be achieved. The most automated personal vehicles on the market perform at level 2, where a human driver still needs to monitor and judge when to take over control, for example with Tesla’s Autopilot. One major challenge towards full autonomy is that the environment (including rules, culture, weather, etc.) greatly influences the level of autonomy that vehicles can safely achieve, and performance in e.g. sunny California, USA, cannot easily be extrapolated to different parts of the world.
Beyond individual personal transportation, other areas in which autonomous vehicles will be deployed include public transportation, delivery & cargo, and specialty vehicles for farming and mining. And while all applications come with their own specific requirements, the vehicles all need to sense their environment, process input and make decisions, and subsequently take action.
Generally, a mixture of passive (cameras) and active (e.g. RADAR) sensors is used to sense the environment. Of all perception sensors, LIDAR is seen by most in the industry as a necessary element. Some are going against this conventional wisdom, including Tesla (relying on cameras RADAR, and ultrasound), Nissan, and Wayve (relying on cameras only).
These sensors are all undergoing technological development to improve their performance and increase efficiency. LIDAR sees the most innovation, as it’s moving away from the traditional, relatively bulky and costly mechanical scanning systems. Newer solutions include microelectromechanical mirrors (MEMS), and systems that do not use any mechanical parts; solid-state LIDAR, sometimes dubbed ‘LIDAR-on-a-chip.’
For higher-level path planning (determining a route to reach a destination), different Global Navigation Satellite Systems beyond the American GPS have become available. By leveraging multiple satellite systems, augmentation techniques and additional sensors to aid in positioning, sub-centimeter accuracy for positioning can be achieved.
Another essential source of information for many current autonomous vehicles are high definition maps that represent the world’s detailed features with an accuracy of a decimeter or less. In contrast, some companies, including Tesla and Apple, envision a map-less approach.
For the whole process of simultaneously mapping the environment while keeping track of location (SLAM), combining data from multiple sources (sensor fusion), path planning and motion control two different AI approaches are generally used:
- Sequentially, where the problem is decomposed into a pipeline with specific software for each step. This is the traditional, and most common approach.
- An End-to-End (e2e) solution based on deep learning. End-to-End learning increasingly gets interest as a potential solution because of recent breakthroughs in the field of deep learning.
For either architectural approach, various types of machine learning algorithms are currently being used: Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Deep Reinforcement Learning (DRL) are the most common. These methods don’t necessarily sit in isolation and some companies rely on hybrid forms to increase accuracy and reduce computational demands.
In terms of processors, most AV companies rely on GPU-accelerated processing. However, increasingly different solutions are becoming available, such as Tensor Processing Units (TPU) that are developed around the core workload of deep learning algorithms. More electronics, greater complexity, and increasing performance demands are met by semiconductor innovations that include smaller components and the use of novel materials like Gallium Nitride instead of silicon. Engineers also face questions about how much to distribute or centralize vehicles’ electrical architecture.
To increase the available data for autonomous driving systems to act upon and increase safety, vehicles need to share information with other road participants, traffic infrastructure, and the cloud.
For this ‘Vehicle-to-Everything’ (V2X) communication, two major networking technologies can be chosen:
- Dedicated short-range communication (DSRC), based on a WiFi standard,
- Cellular V2X (C-V2X), which for AV applications needs to be based on 5G.
At the moment both DSRC and C-V2X are going through enhancements. The question whether DSRC or C-V2X is the best choice is a subject of debate. Due to its rapid progress and performance, the latter is increasingly preferred, and experts express that DSRC won’t sufficiently support some key AV features.
In parallel with technological development, user experience design is an important factor for autonomous vehicles. For lower level automated vehicles, where humans at times have to take control and drive, mode confusion can arise when the state of the vehicle is unclear, e.g. whether autonomous driving is active or not.
Other key challenges for user experience design are trust-building and communicating the intentions of self-driving vehicles. Internally, for the passengers, human driver behavior is often emulated on displays. For external communication companies are researching displays with words or symbols to substitute the human interaction that people heavily rely on when participating in traffic.
Wevolver’s community of engineers has expressed a growing interest in autonomous vehicle technology, and hundreds of companies, from startups to established industry leaders, are investing heavily in the required improvements. Despite a reckoning with too optimistic expectations it’s expected we will see continuous innovation happening and autonomous vehicles will be an exciting field to follow and be involved in.
