Transportation and Planning Impacts


The introduction of autonomous vehicles will undoubtedly change the way people and goods travel. City planners and transportation and traffic engineers will be presented with new challenges and opportunities with the emergence of these vehicles.



Roadways in the automated world would probably look very different due to vehicles being both safer and more efficient. These changes will affect not only affect passengers of a vehicle, but pedestrians as well.

A forecast for the year 2020 demonstrates that most of the mobility infrastructure in American cities will remain largely unaltered five years from now, but usage will undoubtedly continue to change (National League of Cities, 2015).

Roads Built for Human Drivers

Source: Sohrweide, 2018

Existing roadways and intersections are designed mostly to move human-driven cars and trucks. Timed signals guide cars safely along the corridor, which is designed to accommodate peak congestion hours. There are separate, dedicated turn lanes — each with their own markings. As a result, and in order to accommodate pedestrians, refuge spots exist in the middle of each road for pedestrians who cannot make it completely across in one signal phase. Land surrounding the road is used largely for parking lots, which accommodate drivers visiting area stores and restaurants. At the bottom, there is a frontage road for local access.

Roads Built for AVs

Source: Sohrweide, 2018

In contrast, AVs have the potential to travel more precisely than human-operated vehicles, so they could allow for more narrow traffic lanes, reduce the number of lanes needed to accommodate traffic demand, and remove the need for medians and reduce signage. Roadways in an automated world will be smaller with more efficient right of ways. The details can be (Sohrweide, 2018; Lombardo, 2018; Hunt, 2018; ITS International, 2018):

Lane Size: Lanes today are designed to account for driver wander and human error. If lanes were designed to the width of an AV, the lane width could be reduced by 20 percent.

Less Signage: Signs and signals are important features of today’s transportation system as they provide drivers with the information they need to keep the transportation system running smoothly. But with the emergence of V2V and V2I technology, future roadways will not need a large number of traffic signs and signals as information can be transmitted to AVs wirelessly and in real-time.

Traffic Reduction: AVs are able to drive more efficiently than humans can so they can ride closer together, reducing traffic and congestion. Since 93 percent of crashes are caused by human error and 25 percent of congestion is caused by traffic incidents, congestion should be reduced even further with AVs.

Drop-Off Lanes: AVs are also expected to reduce the number of vehicles needed by promoting ride-sharing which will create a need for drop-off lane construction. These will be drop areas that are as close as possible to the entrances of destinations.

Parking Lot Revolution: Since there will be fewer cars on the roads, there will be massive changes made to the location, form and amount of parking needed as AVs can park themselves and remain in the transportation network while waiting for a passenger.

Pedestrians crossing: Today, for safety reasons, most pedestrian facilities exist at controlled intersections. However, this can require an average pedestrian to walk extra blocks to get to their destination. Future pedestrian crossings, following AV implementation, would be moved away from the intersection.

Multiple Land Use: With fleets of driverless cars operating like a modern transit system, AVs are likely to pick up and drop off passengers twenty-four hours a day. The land, then, becomes available for any number of uses.

In the future, there could be far fewer road expansion projects and, with less pavement, a decrease in typical roadway maintenance — the result of fewer lights, less pavement, and no pavement markings. Traffic-related signs would exist largely for pedestrians. The cost-savings on the maintenance side, however, would be balanced by the costs of the technology.  More funding will be needed to install and support high-tech roadways, which might include digital technology in the pavement, sensored lights and other digital components (Sohrweide, 2018).




Safety-related systems for connected vehicle technology will likely be based on Dedicated Short-Range Communication (DSRC). DSRC is fast, secure, reliable and operates on a dedicated spectrum.

Step 1. Camera View

The sensor views the road ahead through use of a camera and identifies:

  • Vehicles
  • Pedestrians
  • Cyclists
  • Lane Markings
  • Speed Limit Signs

Step 2. Data Gathering

Gathering all of this information allows the system to continuously measure the distance and relative speed of the vehicle in relation to other vehicles and pedestrians, the location of the vehicle relative to the lane markings, and the speed of the vehicle. This frequent collection of information is tracked and measured repeatedly.

Step 3. System Decision

The system then determines if there is a potential danger and warns the driver with both visual and auditory alerts. These are uniquely positioned to address some of the main causes of collisions singled out by major automotive safety organizations.

Demonstrations from Mobileye, 2018


The applications described below may be applied by CAVs in the future (Spirent, 2016). The average travel experience would be greatly improved thanks to additional communication and automation provided by CAV technology.

Real-time traffic and incident alerts. CAVs will be cognizant of upcoming traffic and incidents, prompting them to slow down, and/or change lanes or route and adjusting trip time predictions as appropriate. As the technology becomes more sophisticated, signals from other cars already in traffic will begin to inform in-vehicle navigation systems in real time, from average speeds and journey times to activation signals from windscreen wipers and headlights.

Diagnostics and vehicle health reports. CAVs will be able to contact mechanics and garages directly with diagnostic issues, keeping performance parameters under review, and informing the driver of any issues earlier than they would know with a conventional car.

Improved navigation and positioning. Where satellite reception is poor – whether in urban canyons, under heavy tree cover or in tunnels and car parks – the availability of WiFi positioning will enable the vehicle to understand its exact position with drastically better certainty and precision.

Integration with home networks. CAVs will increasingly be able to notify buildings of their approach, perhaps by switching on lights, heating or air conditioning systems. At the same time, it would also be important for homes to exchange information with the vehicle while it is parked outside, for example, transferring downloaded media and journey plans, checking the vehicle’s status current status, including: temperature, oil level, mileage information and journey statistics.

Data exchange with insurers, manufacturers and third parties. Currently, telematics systems tend to store information within the vehicle itself. Two-way communication would likely enable insurers to review usage remotely in real time, allow manufacturers to monitor and refine performance, and permit third-party subscription services to record and analyze telemetry and travel patterns.

Payment integration. CAVs with wireless connectivity would be able to pay remotely for incidental, driving-related costs such as road tolls, parking and even fuel.

Localized information and advertising. There is a potential for motorists to benefit from relevant, highly localized information, warnings and offers; from improved weather and traffic reports to short-term discounts at nearby outlets, fuel price information and parking availability.

Police warnings and location. Vehicle connectivity could enable the police and other authorities to issue targeted warnings to improve safety – whether based upon a defined location or directly to individual vehicles. This would also allow them to locate a connected car for security or recovery reasons.

In-vehicle WiFi hotspot. Whether supplied by the manufacturer or retrofitted, systems are already available to provide local WiFi for passengers’ own handheld and wireless devices – some using the vehicle’s own internet connection. As brands in the space increasingly seek to differentiate themselves, it seems likely that such features could become standardized.

Streaming of music and video on demand. In-car entertainment would probably no longer be governed by physical discs and even mp3 players, while libraries of streaming, on-demand media would give drivers and passengers virtually instant access to countless songs, movies, TV shows and games.

Car-to-car gaming. With CAVs able to communicate wirelessly with each other, the lure of passengers challenging each other to live gaming on their mobile devices could become reality.


With 360-degree vision cameras and precise real-time sensors, CAVs would be able to predict and respond to street activities, like lights, pedestrians, and other vehicles. Various technologies can help direct traffic and redirect vehicles to routes accordingly. Mature vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) technology will enable vehicles to share real-time information regarding mobility with one another and with the surrounding infrastructure.

Several of the safety features of AVs are already in place in testing newer models of vehicles, such as adaptive cruise control and lane keeping. Other anticipated safety features of CAVs, such as high-speed automation and on-highway platooning, will likely be adopted in the next ten years, and may offer further safety benefits. It is worth noting that there are no available statistics showing that CAVs would bring magnificent safety benefits as they are not widely used presently in highway and city networks. However, these safety-oriented technologies have a great potential to improve safety in the future when they have access to mature vehicle monitoring, fast analysis, and a communication system.

Li and Kockelman focused on a number of technologies that may provide the most significant safety benefits. These technologies are mainly to assist the driver, and improved safety is achieved by combining the sensing and threat assessment capabilities of the driver and of the technology. In contrast, when the driver is no longer involved in driving, at the higher automation levels, safety depends entirely on the technology, and that is not yet close to being able to match the safety of the driver alone. These technologies are listed in the table below:

Source: Li and Kockelman, 2016


One CAV will produce 4,000 gigabytes of data per day, from its hundreds of on-vehicle sensors, including camera, radar and lidar. As Nayeem Syed, Assistant General Counsel at Thomson Reuters notes,  “connected car data analytics, critical to both safety and initial viability, will play a key role in driving mass adoption and, when shared effectively, will produce a number of important benefits (Syed, 2017).”

Today’s WiFi-enabled vehicles are connecting to home networks, satellites, and cell towers. Next on tap for vehicle-to-everything (V2X) communication is connectivity with additional elements of the infrastructure, such as traffic lights and signs. Increased networking capabilities will eventually make wireless vehicle-to-vehicle (V2V) communication a reality, enabling cars, trucks, and buses to broadcast data such as position, speed, and brake status to other nearby vehicles so that drivers can be alerted when needed. It’s no surprise that connected and fully automated cars will boast about 3000 Watts of computing power to support all of this advanced functionality.

High-speed serializer/deserializer line drivers and receivers carry the data streams that bring in-vehicle video, audio, and communications to life. These digital content streams not only keep us entertained and navigating in the right direction in our cars, but also improve safety. ADAS applications such as collision avoidance, lane-departure warnings, and self-braking systems rely on fast relay of video streams from multiple cameras and of radar and lidar sensor data to processors, where the data is analyzed and triggers an appropriate action by the vehicle. Mirrors in vehicles would probably be replaced by video links and displays. Randall Wollschlager, Vice President of the Automotive Business Unit at Maxim Integrated notes, “as the automotive industry advances toward fully automated cars, high bandwidth, performance, and reliability of the high-speed links providing the backbone for these applications would be even more critical (Wollschlager, 2017).”


The introduction of automation technology will greatly influence current transport networks, travel behavior, and ridership choices. Real-time data precision will increase intersection and lane capacities, reducing travel time. The preferences and impacts of fully-automated vehicles (AVs) and shared full-automated vehicles (SAVs) will differ for all subgroup categories of demographics and economic backgrounds.


Effects on personal vehicles

  • Human-driven Vehicle → Personal AV: Private vehicles allow for personal comfort/convenience and privacy for the rider. AVs are likely to provide traffic mitigation, social and economic benefits, as well as improved safety to drivers. Surveys have shown expected adoption rate of AVs based on various assumptions (Payre et al., 2017, Fagnant and Kockelman, 2015, Howard and Dai, 2013, Bansal and Kockelman, 2016).  A series of eight test scenarios in the year 2020 setting in the Austin 6-county region’s roadway network suggests that the introduction of AVs (including connected AVs and SAVs) will add 20 percent or more demand for new VMT (Zhao and Kockelman, 2016).
  • Human-driven Vehicle → SAV: Personal vehicle ownership is predicted to fall, owing to the lower costs and greater benefits of SAVs. However, Krueger et al. suggest that individuals whose lifestyles are exclusively restricted to single modality are reluctant to shift to use of shared vehicles (Krueger et al., 2016). Jun et al. provide a large-scale micro-simulation of transportation patterns in the six-county region of Austin, Texas area when relying on a system of SAVs (Jun et al., 2016). SAV mode requests were simulated through a stochastic process for four possible fare levels: $0.50, $0.75, $1, and $1.25 per trip-mile. These fares resulted in mode splits of 50.9%, 12.9 %, 10.5%, and 9.2% of the region’s person-trips, respectively.
  • The Disabled/Elderly/Young  → Personal AV: AVs have the potential to allow the disabled or elderly to travel. Harper et al. estimated a 14% increase of VMT with AVs (fully automated) for the non-driving, elderly and people with travel-restrictive medical conditions using 2009 NHTS data (Harper et al., 2016). Furthermore, the young people who are not old enough to drive may also be able to travel by AV.

Effects on public transit

  • Public Transit → Personal AV: Travel in any form, is a disutility. An individual may choose transit over automobile for a given trip, even though the transit alternative takes longer, if they are able to use the travel time more productively. With the introduction of AVs, however, riding in a personal AV becomes more attractive to the rider than commuting via transit because the rider is freed from driving responsibilities during travel in AVs.  Travelers who take transit due to limited or expensive parking at the destination (e.g. students attending university) may also switch to privately-owned AVs with an empty repositioning trip back (Levin and Boyles, 2015).
  • Public Transit → Automated Public Transit: As vehicles begin to transition to AVs, so will the existing public transit options. Buses, trains, subways, etc. will quickly become driverless, making these modes more efficient than they are today. Many of the passengers that currently use public transit will continue to use this system when it becomes automated (Quarles and Kockelman, 2018).
  • Public Transit → SAVs: Due to the convenience, safety and easy mobility for all that is likely to be supplied by SAVs, the public transit ridership may fall. SAVs can be operated as a part of public transit and be used by public transit operators, as suggested by Kockelman et al., “in the long term, the standards set by the public transportation agencies for C/AV operations will be focused on maintaining adequate levels of transportation capacity and minimizing congestion,” (Kockelman et al., 2016, pg. 100).
  • Other modes → Public Transit: AVs or SAVs have the potential to increase park-and-ride bus or train ridership. SAVs may also increase mobility to transit stops (park-and-ride parking lots) and make the travel time more predictable with a lower price than a whole trip with a SAV. Personal AVs would also allow the disabled or elderly to have access to the public transit. At the same time, AVs or SAVs may also be able to help achieve more complete and mixed-used streets, and also protect vulnerable users (bikes and pedestrians) through automation technologies.

Effects on long-distance travel

  • Buses, Trains → Fleets of AVs: Considering the comfort of the passenger and the fuel efficiency of AVs, it is likely that the passenger will shift from traditional long-distance travel modes to AVs. Bus, train and other car-rental facilities providing long-distance travel services will be affected since the inconvenience of being present during the driving process is eliminated with AVs. Specifically, “fleets of platooning AVs could replace trains as a more fuel efficient and convenient solution to mass long-distance transit,” (Kockelman et al., 2016, pg. 340).
  • Airplanes → Personal AVs: For travel below 500 miles, people are likely to choose AVs over airplanes because they are often the cheaper option, and because the greater travel time is not as much of a concern if it can be spent sleeping or being productive. However, “airlines are still the preferred mode for the longest travel distances,” (LaMondia et al., 2015, pg. 15).  When traveling 500 miles or more, the speed of airplanes wins out over the cost savings of AVs.  Introduction of AVs results in a shift of destination choice, increasing travel in further distances for personal vehicles by 9.6%, and deeply cuts into the number of trips that had formerly been air trips, reducing airline revenues to 53% (Perrine, et al., 2018).


The transportation of goods by truck, train, ship, or aircraft is hugely important to the function of the U.S. economy. Automation will transform the current functionality of freight transportation through automated trucks.

Image result for autonomous truck platooning


Self-driving technology has the potential to transform both passenger and freight transport systems. The trucking industry can expect to see similar benefits like increased safety and better fuel economy. We could expect a paradigm shift in public services, user experiences, mode choices, business models, supply chains and policymaking. As noted by the digital and content marketing company Fronetics, “70% of total U.S. freight tonnage, an equivalent of 10.5 billion tons of freight per year, is transported via trucking. 38 billion gallons of diesel fuel are consumed each year to keep goods moving in the United States. The industry generates $750 billion in revenue each year and is expected to grow rapidly” (Fronetics, 2017).


The stages of automated truck development are shown in the diagram below. We are currently at stage 4, in the process of high automation (Walker, 2018), as we can see examples like highway driving tests (OTTO, 2018) and platooning tests (Daimler, 2018). An article by FleetOwner predicts that “the most expensive stage of the truck automation process, out of the five stages (diagram below) identified by Roland Berger, will be Stage 3, which represents conditional automation in trucks in order to operate within platoons as well as pilot themselves in certain highway scenarios” (Kilcarr, 2016).


Source: FleetOwner, 2016

Once full automation (stage 5) is achieved, self-driving trucks have the potential to reduce the shift or eliminate the responsibilities of the driver. “Fully automated self-driving trucks or Atrucks are those that can leave the truck terminal and travel to a destination without human intervention or presence in the truck cab (Goodwill, 2017). Atrucks may be equipped with other automated functions, like drop-offs and pick-ups, but most experts expect an attendant on board, doing other types of work, sleeping as needed, and ensuring thoughtful deliveries and pickups,” (Huang and Kockelman, 2018, pg. 2). This will lead to a more efficient delivery process, requiring fewer people to be involved, and allowing for increased productivity during travel.

Trucking companies are also preparing for the advent of Atrucks. DHL has already shown the implications on freight travel through the lens of Atrucks and use cases for the logistics industry (Heutger et al., 2014). FedEx is also considering small vehicles that could drive around neighborhoods and make deliveries without human drivers (Woyke, 2017). In 2017, UPS conducted drone delivery trials in Florida in partnership with delivery vehicle manufacturer Workhorse Group (Camhi, 2018).

Emback company’s automated trucks completed a coast-to-coast test run in 2018 from Los Angeles to Jacksonville, Fla (Clevenger, 2018). The modified Peterbilt tractor, outfitted with an array of sensors and guided by self-driving software, traveled 2,400 miles over five days with a driver attended at Level 2 automation.  On November 6, 2017, TuSimple’s SAE Level 4 self-​driving truck got its first public test domestically in the National Intelligent Connected Vehicle (Shanghai) Pilot Zone (TuSimple, 2017). As said on TuSimple’s website, “TuSimple’s computer vision based multi-​​sensor fusion approach uses an array of cameras to scan the surrounding environment, and LiDARs and millimeter-​​​wave radars for redundancy. TuSimple’s camera based solution, with the aid of adaptive headlights, allows the truck to see over 300 meters away at all times. This enables the truck to make safe and efficient decisions.” (TuSimple, 2018)


Driver shortage, as well as the related aging trucker demographic, have been long-standing issues. However, 2018 has seen an improvement in the U.S. economy, coupled with the rapid growth of e-commerce (Prevost, 2018).  Driving jobs must become more appealing and accessible to attract a younger demographic, and the industry must become more efficient, which the automated trucks can help. Automated trucks have the potential to improve efficiency on long-haul routes and the overall trucking cost as well as the total cost of shipped goods. Cheaper goods also lead to larger consumption, and thus drives truck freight volume up. A study from UberATG shows that when 1 million “self-driving” trucks are operating on highways, you would expect to see close to 1 million jobs shift from long haul to local haul, plus about 400,000 new truck driving jobs will be needed to keep up with the higher demand” (UberATG, 2018).

Truck driving jobs scenario, Source: Uber ATG, 2018

As ridesharing company Uber’s Advanced Technologies Group suggests, “every self-driving truck will need partners to cover local routes and bring loads to and from transfer hubs. Growth for self-driving trucks will therefore mean growth for truck drivers, on top of all the things we move getting cheaper and arriving faster. Additionally, those local haul truckers would be picking up and dropping loaded trailers, meaning big reductions in wait times at loading docks. And for drivers who prefer long haul, there will still be many routes across the country for years to come” (Uber ATG, 2018).

However, we still cannot tell exactly how fast self-driving trucks will become part of the industry, or how much impact they will have in the coming years. For example, autonomous delivery robots that have been implemented in a few cities in the US (e.g., DC, San Francisco) and internationally (e.g., London). They have the potential to reduce the labor of truck driver if they can be fully applied in the trucking industry. A startup called Starship Technologies, with offices in London and Tallinn, Estonia, has announced an autonomous delivery robot that promises to do everything that a delivery drone can do (and more), except from the ground and with a realistic chance of actually happening (Ackerman, 2015). Furthermore, Amazon has been granted a new patent by the U.S. Patent and Trademark Office for a delivery drone that can respond to human gestures (Shaban, 2018). Furthermore, the responses from the public vary widely and the technology has some issues in its current state (operations, regulations, etc).

Source: Starship Technologies Autonomous Delivery Robot

Kroger, one of the nation’s largest supermarket chain company, is teaming up with Nuro in June 2018, a two-year-company started by two veterans of Google’s self-driving car team, to launch a fully automated delivery service (Hawkins, 2018). To start out, Nuro will use a fleet of self-driving test vehicles with human safety drivers to make deliveries for Kroger’s grocery stores. Customers can track and interact with the vehicles via a Nuro app or Kroger’s pre-existing online delivery platform.

Source: Nuro Delivery Robot, 2018

Waymo Company has also worked with Walmart and other businesses in the Phoenix, Ariz. area to expand tests of automated vehicles. The 400 “early riders” of Wamyo’s can now take automated vehicles to and from the Walmart in Chandler, Ariz. after purchasing groceries from (Santora, 2018). Toyota and Pizza Hut are launching a partnership that could lead to self-driving pizza cars (Matousek, 2018). Toyota revealed the e-Palette, an autonomous concept vehicle, at the Consumer Electronics Show in Las Vegas on Jan, 2018 and plans to start testing the vehicle as early as 2020. Arlington, Dallas City Council support and encourage private companies in the growing autonomous technology industry to come and test and deploy robotic delivery devices, among which Marble Company is invited (Schrock, 2018).

Source: Marble Delivery Robot, 2018


Another important advancement in the automated freight transportation is automated platooning. This involves a number of trucks following each other at a close range and being connected by vehicle-to-vehicle (V2V) communication. The first truck can be partially automated and manually driven.

The below video is a partially automated truck platooning demonstration that uses V2V technologies like Cooperative Adaptive Cruise Control (CACC) and Dedicated Short Range Communications (DSRC) to maintain speeds, coordinate braking and maintain distances between the trucks. This is a part of the USDOT’s (United Stated Department of Transportation) ongoing efforts to improve efficiency in the nation’s freight transportation network.


Atrucks have the potential to generate a surge in freight capacity by utilizing round-the-clock operations, saving fuel, and improving efficiency, thus reducing the overall cost of commercial freight transport. A research study conducted by The University of Texas at Austin concluded that, “heavy commercial trucks may be the first industry to implement CAV technology in order to increase efficiency. The opportunity for drivers to do other work or rest during long drives may allow heavy trucks to travel for longer periods of time, at lower cost,” (Clements and Kockelman, 2017, pg. 1). The paper also notes that the freight industry can expect to gain more than $100 billion annually due to increased efficiency.


Huang and Kockelman (2017) conducted a research study on truck trade flows anticipation across the U.S after the introduction of automated trucking. They predict that truck flows in U.S in ton-miles will rise up to as much as 11.2%, due to automation’s lowering of trucking costs, while rail flow values fall by 4.8%. Introduction of Atrucks favors longer truck trades, but rail’s low cost remains competitive for trade distances over 3,000 miles. Human-driven trucks will dominate in shorter-distance freight movements, while Atrucks will dominate at distances over 500 miles.


Job Loss: Atrucks have the potential of saving costs in the long run, but agencies will incur high initial capital investments. A series of articles by MBA and graduate students at the University of New Hampshire suggest that automated trucking will not have a very significant negative impact on the labor industry as truck drivers are not limited to driving (Fronetics, 2017), but do other ancillary delivery functions like checking inventory, inspecting loads, and placing orders (Fronetics, 2017). Automated trucks could reduce the demand for drivers by 50-70% in the US and Europe by 2030, with up to 4.4 million of the projected 6.4 million professional trucking jobs becoming redundant (International Transport Forum, 2017). However,  a new report commissioned by the
American Center for Mobility, led by Michigan State University and supported by Texas A&M Transportation Institute shows that only a modest number of truck driver jobs, if any, will be affected. The report notes that, “Due to existing truck driver worker shortages, and the belief that automated technology will largely support truck drivers instead of replacing them, truck drivers are not likely to be displaced in large numbers during the next ten years that the study covered. Also, limousine and bus/transit drivers who are executing services that necessitate face-to-face interaction or passenger assistance, such as luxury services and para-transit, are less likely to be displaced by automated vehicles in the foreseeable future.” (American Center for Mobility, 2018)

Specialized Training: Hardware and software of the Atrucks will have to be periodically maintained to ensure safety. While maintenance can be provided by third party servicers, education and training will be required for the drivers to handle technical hurdles on the road, if any.

Infrastructure Requirements: A report by the American Transportation Research Institute suggests that, “roadway infrastructure conditions, which are generally outside of the control of the private sector, play an important role in the facilitation of AT technologies. For example, lane markings must be visible to video camera systems. Signage must be correct, visible and appropriate. Pavement quality may also cause issues.” (Short and Murray, 2016) Sufficient infrastructure for Atrucks will require investment on the part of the public sector.

Other: In the table below,  ATRI outlines some crucial Government impediments to automated trucking deployment (Short and Murray, 2016).

Source: Short and Murray, 2016, p. 11


You may read about industry-wide benefits on the Economic impacts page. For more information on preferences and mode-choice impacts on long-distance travel, please refer to the mode choice page.



Connected and fully automated vehicle (CAV) technology has the potential to allow vehicles to assist in avoiding collisions, mitigating crash severity, ensuring traffic safety, and reducing crashes due to human error (which is the most prominent contributor to crashes). Many safety benefits of AV technology would save thousands of lives and billions of dollars every year for various industries.

In the US in 2016, 37,461 people were killed in motor vehicle crashes (NHTSA, 2016). 94 percent of these serious crashes can be attributed to human error, which could be greatly reduced if a fully automated fleet is eventually put in place (NHTSA, 2016). Because of this enormous potential to save lives, safety will likely be one of the most important benefits to consider with regard to CAVs. Meanwhile, we would also need to pay attention to the challenges to CAV safety issues, outlined in the Criticisms of AV Ethics and Decision-Making section of the potential challenges page.

Screen Shot 2018-04-26 at 4.33.20 PM

Connected technology is likely to provide essential communication among vehicles, while automation technology may substantially increase safety through automatic identification and judgment. Specifically, automation technology could help increase safety due to (Bhat and Pendyala, 2013):

  • Virtual elimination of driver error
  • Enhanced vehicle control, positioning, spacing, and speed harmonization
  • Offsetting behavior on part of drivers
  • No drowsy drivers, impaired drivers, stressed drivers, or aggressive drivers
  • Reduced number of incidents and network disruptions


Below is a representation of interconnected tasks of systems that would work together to make driving decisions in CAVs.


All of these features would work together in a CAV, serving as the brain of the vehicles. Due to their inherent connectivity, CAVs have the potential to make decisions that are in the best interest of safety.

The NHTSA’s enforcement authority concerning safety-related defects in motor vehicles and motor vehicle equipment extends and applies equally to current and emerging automated driving systems.  The NHTSA outlines 12 safety elements for ADS, listed below (NHTSA, 2017):

  1. System safety
  2. Operational design domain
  3. Object and Event Detection and Response
  4. Fallback (Minimal Risk Condition)
  5. Validation Methods
  6. Human Machine Interface
  7. Vehicle Cybersecurity
  8. Crash-worthiness
  9. Post-Crash Behavior
  10. Data Recording
  11. Consumer Education and Training
  12. Federal, State, and Local Laws

Automakers are urged to consider all 12 safety elements of ADS when developing these systems.


There are significant costs associated with vehicle crashes. These costs can be attributed to the cost of the traffic disruption, the cost of emergency personnel and equipment, the cost of vehicle and infrastructure damage, and more. Decreasing the number of crashes may greatly decrease these expenses. One study from the Center For Transportation Research, The University of Texas at Austin, found that: “about 75% of total (police reported) collision costs could be saved if vehicles were made fully automated and connected,” (Li and Kockelman, 2016, pg. 190). By limiting the frequency and severity of crashes, collision expenses could be drastically reduced.

Using an available crash database, Li and Kockelman found that at a market penetration of 90%, “advanced CAV technologies may reduce current US crash costs at least by $126 billion per year,” (Li and Kockelman, 2016, pg. 17). These savings are mainly due to safety increases from technologies like Forward Collision Warning in combination with Cooperative Adaptive Cruise Control.


The exact safety benefits of AVs are difficult to predict because there is no data and very few AVs to work with for studies. However, through simulations in a study from the University of Texas at Austin, “it was observed that the number of crashes and their severity decreases as the share of AVs in the traffic stream rises,” (Kockelman, et al., 2016, pg. 210). While we would see some safety benefits from automation technology in the near future, full benefits will be likely to occur when all vehicles on the road are automated.


An Uber self-driving test car with one vehicle operator struck a pedestrian on March 18, 2018 in Tempe, Arizona. According to the report from National Transportation Safety Board (NTSB, 2018), the test vehicle was equipped with an automated driving system, consisting of forward- and side-facing cameras, radars, Light Detection and Ranging, navigation sensors and a computing and data storage unit integrated into the vehicle. The original manufacturer Volvo functions, including a collision avoidance function with automatic emergency braking as well as functions for detecting driver alertness and road sign information, were disabled when the test vehicle was operated in computer control mode. The NTSB report (2018), “The pedestrian was dressed in dark clothing, did not look in the direction of the vehicle until just before impact, and crossed the road in a section not directly illuminated by lighting.”

Source: NTSB, 2018

The figure is used by NTSB to illustrate what happened 1.3 seconds before the impact,  “At 1.3 seconds before impact, the system determined emergency braking was needed to mitigate a collision. The yellow bands depict meters ahead of the vehicle, the orange lines show the center of mapped travel lanes, the purple area shows the path of the vehicle and the green line depicts the center of that path.” (NTSB, 2018)

All aspects of the crash remain under investigation as the NTSB determines the probable cause, with the intent of issuing safety recommendations to prevent similar crashes. However, this accident shows the rush of companies to bring vehicles to market, and there remains a lack of clear and sufficient federal regulations around testing and deployment, as AVs come with a promise to save lives by reducing human error and improve safety.



The introduction of fully-automated vehicles (AVs) will lead to tremendous changes to the current U.S. transportation system, VMT, congestion, and traffic flow patterns, among other things. While there are many concerns about the negative traffic impacts of AVs, the automated nature of the vehicles does suggest a possibility of a reduction in long-term congestion if properly implemented.


One of the goals of fully-automated vehicle (AV) technology is to improve current traffic conditions. Among other things, traffic congestion wastes fuel, increases commuters’ time and adds to CO₂ emissions. While AVs are expected to eventually improve congestion, there will likely be a transition period during which vehicle-miles traveled (VMT) will sharply increase, and congestion will be at a high. According to a report from HERE Technologies on congestion impacts due to AVs, “basic levels of autonomy can have a small positive impact in helping to ease traffic congestion, but high levels of autonomy can have a detrimental effect on congestion when their penetration rate is low. For this reason, the early days of highly automated vehicles could present risks to society’s efforts to combat congestion,” (HERE, 2016. pg.8). This concept is illustrated in the graphic below.

Source: HERE, 2016. pg.8

Short Term (5 years)

During the short-term adoption period, with low levels of autonomy, there will be temporary improvements in traffic conditions due to:

  • Advanced Driver Assistance Systems (ADAS) – These technologies reduce human error on the road, thus decreasing congestion. Technologies include:
    • adaptive cruise control
    • automatic emergency breaking
    • lane departure warning

Medium Term (5-20 years)

In the medium term period, with the introduction of level 4 autonomy, it is predicted that congestion will be at its worst for a number of reasons.

  • Driver Adaptation – There will be a transition period during which drivers are becoming accustomed to the new technology. Issues related to this include:
    • new vehicle types  more accidents
    • reliance on autonomous mode & lack of driver intervention  bigger accidents
    • lower comfort level with new technology  slower flow
  • Changes in Travel Behavior – As AVs become more mainstream, we will encounter another set of potential problems due to an increase in convenience. These changes include:
    • long-distance travel will increase  increased VMT & larger vehicles
    • driver drop off & one-way trips  empty driving & increased VMT
    • increased mobility  more vehicles on the road
  • New Policies and Roadway Management
    • easier car-sharing  drop in vehicle ownership
    • managed lanes to increase freeway capacity →  increased congestion on arterial roads (Levin et al., 2018)
    • setting up dedicated ‘Autonomous Lanes’ → preventing negative interactions with traditional cars and also attract market

All of the factors above compound to reduce overall traffic conditions, making the medium term adoption period the least predictable and the most problematic, if policy measures are not taken.

Long-Term (20-50 years)

In the long-term, the traffic condition improvements sought through the development of AVs could eventually be met. This will likely occur when regulation prevents people from owning their private vehicles and requires the use of self-driving mode, “At that stage, central management of all vehicle movements will become feasible, and alongside the elimination of traffic accidents, congestion will become a distant memory,” (HERE, 2016, pg. 11). Efforts to avoid congestion should seek to take full advantage of AVs potential improvements in capacity. Also, central management may take time to tackle liability or freedom issues. Further, central management for large urban area requires high-computationally effective simulators in operation. These conditions will take many years to be fulfilled and will require an effective use of government regulation and policy.


Traffic Waves Reduction

Traffic from human motorized vehicles does not flow smoothly, particularly at high density. When the volume exceeds a certain density, we can notice traffic waves. The instabilities related to traffic flow increase as density increases, “small perturbations amplify and grow into stop-and-go waves that travel backward along the road,” (Stern et al., 2017, pg. 1). These traffic waves can be demonstrated in the following video:

Removing as many human drivers as possible will help to reduce these traffic waves, but according to a study performed by Daniel Work, assistant professor at the University of Illinois at Urbana-Champaign and a lead researcher in the study, “experiments show that with as few as 5 percent of vehicles being automated and carefully controlled, we can eliminate stop-and-go waves caused by human driving behavior,” (Walter, 2017). As driverless vehicles with connectivity become more widespread, traffic conditions may begin to improve.

However, if these automated vehicles are driven autonomously (without connectivity), they will make traffic waves worse than they are now, but if the vehicles are driven cooperatively (using cooperative adaptive cruise control) they can reduce or eliminate the traffic shock waves.  This has been demonstrated in a study by Milanés et al., 2014.

Increase in Lane Capacity

Increases in lane capacity has similar implications to the traffic shock waves described above. Automation allows for an increase in lane capacity because extra space will no longer be needed to account for human error, like distraction and drifting. According to CTR’s report on modifying transportation design, “technical competence and rising confidence in CAV response times can lead to shorter following distances and headways between vehicles,” (Kockelman et al., 2017, pg. 9). Through connectivity, AVs will be equipped to respond to changes in traffic conditions in a more effective way than humans are capable of. If the automated vehicles are not connected they will reduce lane capacity, but if they are connected they can significantly increase lane capacity (see Berkeley, California PATH).

Autonomous Intersection Management (AIM)

At the University of Texas at Austin, there is ongoing research pertaining to optimization of intersection operations depending on what portion of the traffic stream is autonomous (Au, 2018). The AIM system involves V2I communication between AVs and an intersection manager. The intersection manager divides the intersection region into a grid of tiles (divided by space and time) and then specifies the turning movements of AV’s at the intersection (according to a priority function) based on conflicts found with the tiles. This allows for the simultaneous movement of traditionally restricted vehicle movements, thereby increasing the throughput of the intersection. As demonstrated by the following video, delay at these intersections decreases dramatically as the proportion of automated to non-automated vehicles increases.

However, AIM provides little or no improvement over today’s traffic signals when less than 90% of the vehicles are fully-automated, thus restricting its contributions to the transition phases for AVs (Sharon and Stone, 2017, p.1).


People may worry about the safety implications for the real world, as it requires the elimination of all pedestrian and bicyclist usage of the intersection and it assumes perfection of the automated vehicle behaviors and complete predictability of the behaviors of the human drivers. However, AVs do provide potential changes to the urban transportation infrastructure design (see transportation infrastructure impact) and people may expect a long way to achieve AIM via mature technology in the future.

Schaller consulting (2018) released a report related to the Transportation Network Companies (TNC) which could provide new auto-mobility in the future. TNC has already surpasses the ridership on local bus services in the United States, as it offers convenience and flexibility. Considering AVs as personal vehicle use and without public policy intervention, the likelihood is predicted to be more auto-mobility, more traffic, less transit, and less equity and environmental sustainability, where autonomous future mirrors today’s reality. Therefore, policy-makers may need to steer AV development away from this future starting today with steps to manage TNC and personal autos and emphasize frequent, reliable and comfortable high-capacity transit service.