Most metropolitan areas suffer greatly from emissions, noise, crashes, congestion, and the inefficiency and inconvenience of today’s transportation solutions, which are heavily reliant on human-driven, personally owned vehicles. And some question whether adding self-driving vehicles (aka, automated vehicles or AVs) to an already crowded transportation network will only exacerbate these problems.
It’s easy to see why some might think this way, as many of the test AVs on roads today behave somewhat sheepishly. But AVs that have learned to balance human-like assertiveness with machine-like caution can blend seamlessly into traffic alongside human drivers. And that should be our aim.
Like today’s ride-hailing services, future fleets of AVs will help offset transportation inconveniences by offering mobility services on demand—sometimes called “mobility-as-a-service” or “MaaS.” Assuming these fleets are equipped with electric powertrains using centrally located charging infrastructure, AVs can also be expected to reduce emissions and overall traffic noise and support making cities cleaner and quieter.
What’s more, AVs are expected to be at least three orders of magnitude safer than today’s human-driven vehicles, especially those with a truly redundant sensing system. Plus, self-driving vehicles don’t fall asleep while driving, don’t text or get distracted, don’t need a second or longer to react, don’t get agitated or angry, don’t have a narrow field of view with two eyes looking only in one direction, don’t drink or take drugs, and don’t engage in reckless or careless behavior, such as speeding and following other vehicles too closely.
But unlike today’s on-demand mobility services, which have been blamed for clogging metropolitan areas like New York, San Francisco, London, or Los Angeles, AVs will significantly improve the efficiency of today’s transportation networks, distribute traffic intelligently and evenly across the road network in real time, and thereby reduce congestion and optimize traffic flow.
A mobility intelligence platform (an AI-powered brain) is the key. Using machine learning to predict traffic and rider demand, the AI brain can help AVs know precisely where to be to best serve the community. A plethora of real-time information can be considered, such as traffic flow, incidents, weather, events, historical data, and more, which together can improve the efficiency of the transportation network, lower the waiting time for users, and increase convenience, quality of service, and customer satisfaction.
Future AV fleets will take on a variety of shapes, sizes, and services. Consider that self-driving shuttles can operate on demand or in fixed corridors to connect people to major transit hubs like bus and train stations. They can be small, individual rider or family vehicles, mini-buses, or larger vehicles as needed to meet local demand. Such first-/last-mile services can increase the use of public transportation and ensure that it is modernized and well maintained with a high quality of service.
Mobility intelligence can also be used to tailor solutions for the unique infrastructure and challenges of every metropolitan area. Without the cost considerations of a human driver, large buses can be replaced with smaller self-driving shuttles, thereby enabling transportation network providers to increase ride frequency and react dynamically to demand.
Smaller vehicles for ride-hailing and ride-pooling, where about 70-80% of the total operations costs are the driver fees, can—over time—also be augmented or replaced with self-driving taxis and limousines. This more private option may be especially welcome following the COVID-19 pandemic, offering passengers a greater level of comfort with reduced risk of infection, greater convenience, and overall lower-cost door-to-door mobility solutions.
Another very important aspect is accessibility. As part of a dynamic, multimodal transportation network, self-driving vehicles will enable inclusive mobility for the visually impaired and those with service animals. Ramps and wheelchair access should become standard in self-driving shuttles, and the user experience should be optimized to fit the needs of everyone: people with and without disabilities, children, and the elderly. As AVs proliferate, the need for a driver’s license in order to enjoy the freedom of individual mobility will fade away over time.
In the goal to reduce congestion and make transportation more efficient and convenient, AVs will be just one tool in the multimodal toolkit. Municipalities and fleet operators need to find the right balance of fleet vehicle and service types along with other ways of moving people and goods. Bicycles, e-scooters—including dedicated lanes and pathways—carpooling and sharing, all forms of public transportation, and potentially future 3D-mobility solutions must all be woven together using AI-powered mobility intelligence.
Mobility is as personal and unique for every person as the clothes or jewelry they wear, the bag or purse they carry, or the smartphone they use. One size does not fit all. And digitizing transportation and offering each individual tailor-made mobility solutions even depending on time of the day, the day of the week, or the purpose of the trip will be very powerful and impactful.
What people need and want is to get safely to their destination at acceptable speed and cost and with the expected convenience and service. Multimodal urban mobility—with AVs woven seamlessly into this fabric—will support solving the challenges of urban transportation around the globe.
There is much work to do before AVs can proliferate: legislation and regulation, support from local governments, development and installation of operating infrastructure, and more. The transformation of the transportation network including self-driving vehicles will be no less significant than the shift from horse-drawn carriages to the automobile. But when AVs are woven into the fabric of transportation networks, mobility will finally be accessible, efficient, convenient, clean, safer, more affordable, and maybe even enjoyable in ways we really cannot imagine yet.