Collaborative Autonomous Cars Planning for Los Angeles In the Future
Traffic jams is always acute pain for Los Angeles; will the introduction of autonomous cars relieve or worsen the traffic conditions in LA? It is impossible to quantify the impact of autonomous cars at the early stage without taking into consideration several factors. However, initial studies have predicted negative side-effects: fully autonomous vehicles could boost increase of travel by people, for example, which could worsen the congestion.
LA has been confronted with challenges before, as with the big smog, car air pollution and social inequity, now the automation. This article is going to talk about the possible negative impacts caused by autonomous cars, and the recommended action to governments and decision makers to overcome the negative effect. It will demonstrate a good example for other cities to handle the future of autonomous vehicles in the country.
Los Angeles is well known for its one of the most world’s traffic congestion. According to INRIX’s Global Traffic Scorecard, the average time spending in traffic jams for an Angelinos is 104 hours in 2016. The cost of congestion, measured in wasted time and fuel, is estimated to $9.7 billion for Los Angeles, and works out to $300 billion for across the nation.
Los Angeles has a reputation as an automobile-centered city and region, and arguable should be the vanguard for the adoption of autonomous vehicles as the next revolution. Google cars, which drives itself at a low speed, has already showed in significant numbers of self-contained campuses at universities (Townsend, 2016) Uber’s first driverless vehicle, custom Volvo XC90s, is launched in Pittsburgh during August of 2016. Nissan and Mercedes Benz also brought their driverless car models to the showrooms in Southern California years ahead of schedule in 2018.
But the fully autonomous experience on all roads is still some time away as the technology continues to evolve and improve.
According to NHTSA (National Highway Traffic Safety Administration), the process of autonomy can be divided into five levels that the industry will progressively realize to take controls away from the drivers. First level of autonomy refers to driver assistance system installed to the car, such as Adaptive Cruise Control (ACC), Automatic Emergency Braking (AEB) or Lane Keep Assistance (LKA). Level 2 to level 4 autonomy partially take away some controls from the driver's, Level 5 or fully automation experience free up the driver to undertake other duties during the trip.
Both of the factors introduced before would variably impact traffic congestion. This picture shows the combined impact from the two factors on the traffic congestion in the short term and long term.
In the short term (5 years), many advanced driver assistance systems already offer quantifiable benefits in terms of accidents reduction and improvements in traffic efficiency. For example, Eyesight technology released by Japanese vehicles claims to reduce the accidents rate to 40%. Other ACC (Adaptive Cruise Control) systems are showing how they can improve the vehicle density and flow rates on the road.
In the medium term (5–20 years), there is a pretty big window of time for a market change, there is the possibility that full autonomy will only see low adoption, which could cause negative impacts on the traffic.
a. More Accidents
The first possible consequence is that, small amount of highly autonomous cars among millions traditional cars would surprise regular drivers with different behavior mode of AV. As a result, more unanticipated accidents are created.
b. Slower Flow Rate
To improve the comfort of passengers, autonomous cars may use slower acceleration and deceleration rates, which could lead to overall flow of the road decreasing.
c. More Cars
The third consequence is the increasing ownership of cars since autonomous cars extend the range of customers to youth, elderly, and disabled who cannot drive in the past. Besides, many zero occupancy driverless cars are jammed with other passenger cars on the roads, since people start to use autonomous cars to run errands for them.
d. More Travel
As the number of fully autonomous cars rise, travel patterns of people may change. People are more likely to extend their trip when they can talk, text and even sleep behind the wheel. People would move away from urban cities, and suburban roads traffic are burdened.
This article’s purpose is to overcome the risks stem from the medium term, when the roads are filled with heterogeneous vehicles, autonomous and traditional ones.
Policy Recommendation — Collaborative Autonomous Cars
The negative impacts brought by autonomous cars is due to the failed interaction between them and the regular traffic system. Each tech and automotive company is developing their technologies in competition, raising serious questions about interoperability. So the key is to improve the collaboration functions of autonomous cars. Many car manufacturers are currently put priorities on autonomy development, but neglects the communication of the car to other fleets on the road. A collaborative autonomous car could identify and minimize the risks I have talked above. The following introduces a few features of a collaborative autonomous car.
Bridge the gap between autonomous and traditional cars through communications. V2V can provide a bridging mechanism with traditional cars. The advanced sensors fitted into autonomous cars will be able to collect and distribute valuable information that could and should be used to also support traditional cars. Autonomous Vehicle-to-Traditional-Vehicle (AV2V) communications will therefore become an important collaborative priority in the coming years and it will be important to explore how this hybrid ecosystem can work together more effectively.
We assume that there are several vehicle fleets work within a region, when a customer sends a request, the operator would control the assignment of vehicles to customer in real-time, so that low customer wait times are achieved and operational costs are minimized. Car sharing has already proven to remove the number of vehicles required to serve the same number of people and this will be important in addressing congestion in a growing city.
V2I Communication (Vehicle to Infrastructure)
The proposed California Freeway 2.0 Act of 2030 indicated that relying solely on Vehicle-to-Vehicle communication is not enough for accuracy and safety. Smart road system development would allow warnings and further condition information to spread more widely and rapidly.
It would allow for top-down interventions to address chronic congestion points or crisis incidents. And it would provide the massive movement data needed to power the comprehensive regional transportation model.
An open platform for data sharing
An aggregation platform is required to gather and process the data at-scale. The data collected from roads, vehicles and infrastructures such as traffic lights or potholes, should be collected at data management centers and delivered to governments, DOTs or other vehicles timely, so that the road network could be safer and more efficient. Imagine a pothole detected by a vehicle sensor is automatically sent to the road authority, notifying the agency of the need for repair.
Next Step and Timelines
The City of Los Angeles should designate a research group to build a computerized simulation of the traffic congestion affected by the two factors, different levels of autonomy and adoption rates, in order to find out how the congestion degree is changed.
Secondly, the City of Los Angeles should invest in the construction of smart roads to enable the V2I Communication, and develop open platform of data sharing to optimize the efficiency of the overall road network.
To implement the collaborative strategy, what is needed is a concerted effort to shift “each of their own” autonomy to collaborative autonomous cars; to bridge the autonomous and non-autonomous cars through vehicle to vehicle communication, and enable collaborative management of the road network through open platform of data sharing among vehicle data, road data and infrastructure data such as stop light information. In this way, the negative impacts from medium term autonomous stage could be minimized, and the full benefits of autonomy will be truly realized.