A Deep Dive into Self-Driving Cars
By: Wenqi Gao
Tags: Autonomous Cars, Self Driving Cars, Artificial Intelligence, Data Science, Autonomous Vehicles

History of Self-Driving Cars

With the rapid development of technology and urbanization, there is growing demand for traveling in a more convenient, labor-efficient and safe way. One of which is through self-driving cars.

Scientists and tech corporations in a multitude of industries have been exploring the automation of vehicles for a long time. There’s data showing that experiments have been conducted on self-driving cars since at least the 1920s and people never stopped to optimize and explore this field since then. As of 2019, twenty-nine U.S. states have passed laws permitting autonomous cars. In Europe, cities in Belgium, France, Italy, and the UK are planning to operate transport systems for driverless cars, and Germany, the Netherlands, and Spain have allowed testing robotic cars in traffic.

Self-Driving cars in Industry

There’s data showing that 40+ corporations are working on autonomous vehicles. Beyond trendy names like Tesla and Alphabet chasing self-driving cars, a host of auto brands and other tech heavyweights are also investing in autonomous R&D. Many major automotive manufacturers, including General Motors, Ford, Mercedes Benz, Volkswagen, Audi, Nissan, Toyota, BMW, and Volvo, are in the process of testing driverless car systems.

Private companies working in auto-tech are attracting record levels of deals and funding, with autonomous driving startups leading the charge. Along with early-stage startups, VCs, and other investors, large corporations are also angling to get a slice of the self-driving pie.

The operating systems of level 4 (see below for an explanation on the levels) are currently being tested by Google, Uber, Apple, and Samsung. These systems have already been tested in trucks by Volvo, Otto (Uber owned) and Daimler (Mercedes Benz).

The Five Stages of Self-driving Cars

Society of Automobile Engineers’ International standard J3016 defines six levels of automation for automakers, suppliers, and policymakers to use to classify a system’s sophistication. The pivotal change occurs between Levels 2 and 3, when responsibility for monitoring the driving environment shifts from the driver to the system.

Level 0: As usual, a human driver is required to do all tasks related to operating the vehicle safely at all times.

Examples: A 1967 Porsche 911, a 2018 Kia Rio.

Level 1: An automated system on the vehicle can sometimes assist the human driver.

Example: Adaptive cruise control.

Level 2: An automated system on the vehicle can actually conduct some parts of the driving tasks while the human driver monitors the environment and performs the rest of the required driving tasks.

Examples: Audi Traffic Jam Assist, Cadillac Super Cruise, Mercedes-Benz Driver Assistance Systems, Tesla Autopilot, Volvo Pilot Assist.

Level 3: An automated system can do both actually conduct some parts of a driving task and monitor the driving environment in some instances, but the human driver must be ready to take back control when the system requests.

Example: Audi Traffic Jam Pilot.

Level 4: The car is capable of performing all safety-critical driving functions while monitoring conditions in defined use cases. Self-driving is fully possible in most road conditions and environments without the need for human intervention. A functional driver cockpit is still in place (steering wheel, brake/acceleration pedal, etc.). An automated system can both conduct the task of driving and monitoring the environment, without the need for a human driver to take back control. However, the operation of the system is limited to certain environments and conditions.

Example: Google’s now-defunct Firefly pod-car prototype, which had neither pedals nor a steering wheel and was restricted to a top speed of 25 mph.

Level 5: The vehicle in this stage can be called an actual “driverless-car” and is completely driverless, full-time automated driving in all conditions without a human driver. These vehicles will not feature driving equipment and will no longer look like the vehicles of the past. An automated system that can perform all driving tasks, under all conditions that a human driver could perform.

Example: None yet, but Waymo — formerly Google’s driverless-car project — is now using a fleet of 600 Chrysler Pacifica hybrids to develop its Level 5 tech for production.

Image from: Internet of Business

Challenges to Self-Driving Cars

It is obvious that we still have a long way to go before reaching the final stage, as there are difficulties still very much up in the air. Ford CEO Jim Hackett expressed a conservative stance, admitting that the company had initially “overestimated the arrival of autonomous vehicles.” It still plans to launch its first self-driving fleet in 2021, but with significantly dialed-back capabilities.

As for the intelligence, practicability, maneuverability and safety of autonomous driving, there are still many difficulties in science and technology. At the same time, it is hard to achieve a fully autonomous driving infrastructure and develop legal provisions applicable to the traffic environment with driverless cars.

Image from: RAC

Road Perception: Cognition while driving is complicated. It is challenging to build a self-driving system that can perceive the road better than the best human driver. A self-driving car’s perception system needs to ensure a lower car fatality rate, even though the current car fatality rate is already low (about one death per 1 million hours of driving).

Cognition while driving is complicated. It is challenging to build a self-driving system that can perceive the road better than the best human driver. A self-driving car’s perception system needs to ensure a lower car fatality rate, even though the current car fatality rate is already low (about one death per 1 million hours of driving). Navigation: A perfect navigation system is essential for the higher stages of autonomous vehicles. It is difficult to build a navigation system that is updated in real time, intelligent and flexible, covering all geographical areas as well as all the traffic information.

Image from: IEEE Computer Society

Performance: Developing a perfect internal system is challenging. The high-quality power management is needed to control power, overall consumption, and thermal dissipation. There is always a trade-off between safety and efficiency. For example, for the automatic driving system to make decisions while driving, it is safe for the system to decide to drive very slowly. However, for the customers, a very slow and inefficient car is useless and tedious.

Developing a perfect internal system is challenging. The high-quality power management is needed to control power, overall consumption, and thermal dissipation. There is always a trade-off between safety and efficiency. For example, for the automatic driving system to make decisions while driving, it is safe for the system to decide to drive very slowly. However, for the customers, a very slow and inefficient car is useless and tedious. Handling the Unexpected : The driving regulations in the real traffic environment are very complicated. It is difficult for the automatic driving system to understand the complicated traffic regulations as a reasonable person does, and make the most appropriate choice in some special emergencies, including the choice between humanity and rationality. For example, what happens when there is a conflict between following the traffic rules or reducing casualties when other vehicles or pedestrians do not follow the traffic rules?

: The driving regulations in the real traffic environment are very complicated. It is difficult for the automatic driving system to understand the complicated traffic regulations as a reasonable person does, and make the most appropriate choice in some special emergencies, including the choice between humanity and rationality. For example, what happens when there is a conflict between following the traffic rules or reducing casualties when other vehicles or pedestrians do not follow the traffic rules? Liability : There is still a great deal of controversy over the possible liability for car accidents. Who is responsible for the accident — the car manufacturers, the retail company or the owner? Is it the operating system or hardware that’s at fault? Should we apply strict liability to the manufacturer to ensure they are prudent enough while building the car? If so, if the companies are willing to shoulder that liability? These are just some of the questions we face while building self-driving cars.

: There is still a great deal of controversy over the possible liability for car accidents. Who is responsible for the accident — the car manufacturers, the retail company or the owner? Is it the operating system or hardware that’s at fault? Should we apply strict liability to the manufacturer to ensure they are prudent enough while building the car? If so, if the companies are willing to shoulder that liability? These are just some of the questions we face while building self-driving cars. Consumer Trust: The public lacks trust in the automated system for safety reasons, and most people tend to trust their driving skills, instead of completely trusting and relying on artificial intelligence.

Image from: Seagate Blog

There remain a multitude of challenges facing the development of fully autonomous driverless cars. Yet one thing is for certain: the driverless car industry will continue to expand with an optimistic future. Many companies around the world are striving to align their business models accordingly with this innovation of self-driving cars, which includes hiring highly sought-after yet low-supply talent in this area.