“The future ain’t what it used to be.” Yogi Berra
We are in the midst of a massive revolution that will dramatically transform ground transportation. It is anticipated that autonomous vehicles (or driver-less cars) will be commercially available by 2020, if not sooner. By
A wave of new transportation technology is coming to Columbus after the city won the federal Smart City Challenge. The grant money will usher in driverless cars but could end the idea of rail as a mass-transit option. “The City of Columbus plans to leap-frog fixed rail” by using new modes of transportation, Columbus says in the U.S. Department of Transportation application. The city last month won a $40 million grant from the U.S. Department of Transportation, besting cities like San Francisco and Portland, Oregon. They already have rail options and still struggle with traffic congestion. Those cities are also larger and attract far more visitors to their cores. The fact that Columbus is without rail might actually have helped its case in the smart-city competition, as it is the test case for new transportation methods that could scale to similar cities. Columbus is the biggest city in the U.S. to not offer rail service – something like light rail, streetcars, monorail – as a mass transportation option. The city’s application said its bus-based mass transit system, operated by the Central Ohio Transit Authority, can “demonstrate emerging mobility solutions at a lower cost and with greater flexibility than a fixed-rail infrastructure.” — Columbus will ‘leap-frog’ light rail as transit option after Smart City Challenge win
According to Philippe Crist, an economist with the Organization for Economic Co-operation and Development (OECD) “Fleets of shared, self-driving vehicles could indeed remove nine out of every ten vehicles on city streets, eliminating the need for all on-street parking and 80% of off-street parking, according to a recent study by the group.” — Urban Transit’s Uncertain Future
The rise of a “taxibot” may further reduce the need for car ownership and enable the sharing of vehicles that make shared, self-driving vehicles possible. According to Emilio Frazzoli, head of Future Urban Mobility for the Singapore-MIT Alliance for Research and Technology “You couldn’t have imagined this ten years ago when people didn’t have smart phones and mobile computing was not available. Now you have this ability to connect and book a car. You see it with Uber and the proliferation of taxi booking apps or public transportation schedule routing apps, and this is at the same time you have autonomous vehicle technology that is evolving. You can marry the two.” — Urban Transit’s Uncertain Future
If you doubt the accelerating adoption of new technology, it is worth to pause for a moment and consider that the iPhone was introduced in June 2007 and now is a ubiquitous device that has fundamentally transformed entire industries. The mass adoption of new technologies continues to accelerate. One recent estimate suggests that the typical luxury sedan now contains over 100 MB of binary code spread across 50–70 independent computers.
An analysis of the history of technology shows that technological change is exponential, contrary to the common-sense “intuitive linear” view. So we won’t experience 100 years of progress in the 21st century — it will be more like 20,000 years of progress (at today’s rate). — The Law of Accelerating Returns
Uber and Gilt are selling passes for unlimited uberPOOL rides in New York City. “The deal is being called a “commute card” and can only be used Monday through Friday during commuting hours (7-10am and 5-8pm) in Manhattan. These are the same hours during which Uber offers $5 flat rate uberPOOL rides in NYC. As a refresher, uberPOOL is Uber’s carpool product where the company matches you with riders headed the same direction … this deal means commuting in an uberPOOL is cheaper than taking the subway.”
Uber and Lyft are looking beyond competition with traditional taxi services. They may be creating the first practical, affordable personal rapid transit (PRT) systems that will compete with buses. In 2014, Uber launched UberPool, enabling multiple parties to share a ride along similar routes. The following year, the company announced uberCOMMUTE in China, which they described as ” carpooling at the press of a button.” In the U.S., it’s being tested in Chicago. Then, in December, Uber launched uberHOP in Seattle, which operates along pre-selected commuters routes.
Virtually all mass transit systems are publicly subsidized. Farebox revenues rarely cover more than 50 percent of expenses, which are labor and capital-intensive. In Pinellas Park, Florida—a Tampa suburb—has just replaced two bus lines with Uber service, subsidized to the tune of $3 per ride. It’s cheaper than running the buses. The Pinellas Suncoast Transit Authority budgeted $40,000 a year. Running the two bus lines cost four times as much. — Uber and Lyft Revolutionize Public Transit
A recent study by the Boston Consulting Group found the cost of conveying one passenger by an autonomous vehicle would be 35% less than by conventional taxi at the average taxi occupancy rate of 1.2 passengers. Increase an autonomous vehicle’s rate of occupancy to just two passengers and the cost per passenger becomes competitive with mass transit. — Urban Transit’s Uncertain Future
“… private companies like Uber, which recently began test-driving its own autonomous vehicle, could drive down the cost of shared transport to such a point that car ownership wouldn’t be worth it.” — Urban Transit’s Uncertain Future
In addition, autonomous vehicles will greatly enhance mobility for transit dependent populations that may be disabled, too young or too old. For example, in the US there are approximately 36 million people with disabilities. Given the mobility and autonomy of this new technology, this will improve utilization of assets like vehicles, roadways and parking lots to further reduce the cost of these services by providing better efficiency.
U.S. transportation chief visits Google to unveil 30-year plan
“We’ve got to look at our own regulatory framework … to make sure we’re being as nimble and flexible and adaptive as we can be. … That’s what the future is demanding,” Foxx said. Foxx and Schmidt took a quick ride in the tiny electric-powered pod that dropped them off at an entrance to the corporate campus. It then drove away on its own. “This is awesome, this is cool,” Foxx remarked as Schmidt and Chris Urmson, the head of Google’s self-driving car project, showed him how it worked.
Autonomous vehicles will be a disruptive innovation with major implications for society. requiring policy makers to address many unresolved questions about their effects. One fundamental question is about their effect on travel behavior. It will be easier to share cars and that this will thus discourage outright ownership and decrease total usage, and make cars more efficient forms of transportation in relation to the present situation. Autonomous vehicles may reduce public transit travel demand, leading to reduced service.
Think that’s unlikely? Many companies are investing heavily in this area and it will have a massive impact on how we move people (and things). Several companies have already announced that they will have partially autonomous vehicles (Level 3) ready in the next 5-6 years including Audi. Baidu, BMW, Ford, Google, LeTV, Mercedes, Nissan, Tesla, Uber
The avionics system in the F-22 Raptor, the current U.S. Air Force frontline jet fighter, consists of about 1.7 million lines of software code. The F-35 Joint Strike Fighter, scheduled to become operational in 2010, will require about 5.7 million lines of code to operate its onboard systems. And Boeing’s new 787 Dreamliner, scheduled to be delivered to customers in 2010, requires about 6.5 million lines of software code to operate its avionics and onboard support systems.
These are impressive amounts of software, yet if you bought a premium-class automobile recently, ”it probably contains close to 100 million lines of software code,” says Manfred Broy, a professor of informatics at Technical University, Munich, and a leading expert on software in cars. All that software executes on 70 to 100 microprocessor-based electronic control units (ECUs) networked throughout the body of your car. — This Car Runs on Code
Disruptive innovation in terms of low-cost and high-quality can shape the market even before the launch. One such example is the technology developed by a 19-year-old Romanian high-school student, Ionut Budisteanu, who created a camera and radar system for autonomous cars that costs a fraction (10%) of the cost for the existing solutions. Or Edgar Sarmiento, a 24-year-old from Columbia, who designed a self-driving minibus and built it in weeks with Local Motors. Or recent advances by MIT which has reduced the large and expensive LIDAR to lidar-on-a-chip system that is smaller than a dime, has no moving parts, and could be mass produced at a very low cost to be used in self-driving cars, drones, and robots.
What is an Autonomous Vehicle?
In the United States, the National Highway Traffic Safety Administration (NHTSA) has proposed a formal classification system:
- Level 0: The driver completely controls the vehicle at all times.
- Level 1: Individual vehicle controls are automated, such as electronic stability control or automatic braking.
- Level 2: At least two controls can be automated in unison, such as adaptive cruise control in combination with lane keeping. Many of these features are available in cars today.
- Level 3: The driver can fully cede control of all safety-critical functions in certain conditions. The car senses when conditions require the driver to retake control and provides a “sufficiently comfortable transition time” for the driver to do so.
- Level 4: The vehicle performs all safety-critical functions for the entire trip, with the driver not expected to control the vehicle at any time. As this vehicle would control all functions from start to stop, including all parking functions, it could include unoccupied cars.
An increase in the use of autonomous cars would:
- Increased roadway capacity and reduced traffic congestion due to reduced need for safety gaps and the ability to better manage traffic flow.
- Reduce total number of cars by increased car-sharing, since an autonomous car can drop off a passenger at one location and go to a different location to pick up another. Also see Uber perpetual rides.
- Higher speed limit for autonomous cars.
- Greater efficiency with coordinate platooning using vehicle-to-vehicle and vehicle to infrastructure communications allowing for drafting, better mileage efficiency, faster transit times and coordinated traffic signaling.
- Time-shifting freight traffic to off-peak hours, reducing congestion during peak travel times and increasing highway capacity.
- Alleviation of parking scarcity, as cars could drop off passengers, park far away where space is not scarce, and return as needed to pick up passengers.
- Reduction of physical space required for vehicle parking.
- Elimination of redundant passengers – the robotic car could drive unoccupied to wherever it is required, such as to pick up passengers or to go in for maintenance. This would be especially relevant to trucks, taxis and car-sharing services.
- Fewer traffic collisions, since unlike a human driver with limited situational awareness an autonomous car can continuously monitor a broad range of sensors (e.g. visible and infrared light, acoustic incl. ultrasound) both passive and active (LIDAR, RADAR) with a 360° field of view and thus more quickly determine a safe reaction to a potential hazard, and initiate the reaction faster than a human driver.
- Avoid traffic collisions caused by human driver errors such as tail gating, rubbernecking and other forms of distracted or aggressive driving.
- Relief of vehicle occupants from driving and navigation chores.
- Removal of constraints on occupants’ state – in an autonomous car, it would not matter if the occupants were minors, elderly, disabled, unlicensed, blind, distracted, intoxicated, or otherwise impaired.
- Reduction in the need for traffic police and premium on vehicle insurance.
- Reduction of physical road signage – autonomous cars could receive necessary communication electronically (although physical signs may still be required for any human drivers).
- Smoother ride.
- Reduction in car theft, due to the vehicle’s increased awareness.
- Removal of the steering wheel and remaining driver interface saves cabin space and allows a cabin design where no occupant needs to sit in a forward facing position
Individual vehicles may also benefit from information obtained from other vehicles in the vicinity, especially information relating to traffic congestion and safety hazards. Vehicular communication systems use vehicles and roadside units as the communicating nodes in a peer-to-peer network, providing each other with information. As a cooperative approach, vehicular communication systems can allow all cooperating vehicles to be more effective and increase efficiency of our existing roadway infrastructure thereby dramatically reducing traffic congestion. According to a 2010 study by the National Highway Traffic Safety Administration, vehicular communication systems could help avoid up to 79% of all traffic accidents.
In 2012, computer scientists at the University of Texas in Austin began developing smart intersections designed for autonomous cars. The intersections will have no traffic lights and no stop signs, instead using computer programs that will communicate directly with each car on the road.
Congestion and traffic operations can be reduced using autonomous vehicle through the use of sensors that can sense traffic flows by monitoring vehicle braking and acceleration through V2V monitoring. V2I monitoring can also be used to improve flow and safety in intersections and high-problem areas. These systems will utilize information from other vehicles, smart traffic systems and other forms of smart infrastructure, allowing for a much higher throughput of traffic and further reducing the risk of accidents through the use of predictive trajectory modeling.