Analysis of Autonomous Vehicle Policies

The rapid development and adoption of connected and autonomous vehicles will transform the U.S. transportation system over the next 30 years. Although the widespread use of fully connected and autonomous vehicles is still several years away, it is nonetheless critical that legislators, policymakers, and regulators understand how the presence of these vehicles will restructure the operation of roadway networks. Without a doubt, lawmakers and policymakers will need to develop new laws — and revise existing ones — to account for the presence of connected and autonomous vehicles. This report provides an overview of connected and autonomous vehicles — their positives and negatives, how quickly consumers may adopt them, and strategies state departments of transportation can use to streamline their deployment — before examining the recent efforts at the federal and state level to establish laws and regulations that will smooth the transition for connected and autonomous vehicles. Federal agencies, such as the National Highway Traffic Safety Administration, have issued guidance for manufacturers and state and local governments. Meanwhile, numerous states have enacted or proposed legislation to deal with connected and autonomous vehicles. The content of these statutes varies among states, however, shared areas of concern include defining what constitutes an autonomous vehicle, establishing basic protocols for testing autonomous vehicles on public roadways, specifying under what circumstances a manufacturer is liable for crashes, and setting guidelines for operating autonomous vehicles. Looking toward the Commonwealth, a review of the Kentucky Revised Statutes and Kentucky Administrative Regulations identifies current policies and regulations that may apply to connected and autonomous vehicles. Policymakers and legislators will likely have to grapple with issues pertaining to licensing, registration, cell phone usage, and the definition of vehicle operators in the context of autonomous vehicles. Given that the proliferation of connected and autonomous vehicles will accelerate over the next 10-15 years, Kentucky will benefit if these issues are addressed and resolved in a timely manner

Cooperative merging strategy between connected autonomous vehicles in mixed traffic

In this work we propose a new cooperation strategy between connected autonomous vehicles in on-ramps merging scenarios and we implement the cut-in risk indicator (CRI) to investigate the safety effect of the proposed strategy. The new cooperation strategy considers a pair of vehicles approaching an on-ramp. The strategy then makes decisions on the target speeds/accelerations of both vehicles, possible lane changing, and a dynamic decision-making approach in order to reduce the risk during the cut-in manoeuvre. In this work, the CRI was first used to assess the risk during the merging manoeuvre. For this purpose, scenarios with penetration rates of autonomous vehicles from 20% to 100%, with step of 10%, both connected and non-connected autonomous vehicles were evaluated. As a result, on average a 35% reduction of the cut-in risk manoeuvres in connected autonomous vehicles compared to non-connected autonomous vehicles is obtained. It is shown through the analysis of probability density functions characterising the CRI distribution that the reduction is not homogeneous across all indicator values, but depends on the penetration rate and the severity of the manoeuvre

Cooperative merging strategy between connected autonomous vehicles in mixed traffic

In this work we propose a new cooperation strategy between connected autonomous vehicles in on-ramps merging scenarios and we implement the cut-in risk indicator (CRI) to investigate the safety effect of the proposed strategy. The new cooperation strategy considers a pair of vehicles approaching an on-ramp. The strategy then makes decisions on the target speeds/accelerations of both vehicles, possible lane changing, and a dynamic decision-making approach in order to reduce the risk during the cut-in manoeuvre. In this work, the CRI was first used to assess the risk during the merging manoeuvre. For this purpose, scenarios with penetration rates of autonomous vehicles from 20% to 100%, with step of 10%, both connected and non-connected autonomous vehicles were evaluated. As a result, on average a 35% reduction of the cut-in risk manoeuvres in connected autonomous vehicles compared to non-connected autonomous vehicles is obtained. It is shown through the analysis of probability density functions characterising the CRI distribution that the reduction is not homogeneous across all indicator values, but depends on the penetration rate and the severity of the manoeuvre

6G Cellular Networks and Connected Autonomous Vehicles

With 5G mobile communication systems been commercially rolled out, research discussions on next generation mobile systems, i.e., 6G, have started. On the other hand, vehicular technologies are also evolving rapidly, from connected vehicles as coined by V2X (vehicle to everything) to autonomous vehicles to the combination of the two, i.e., the networks of connected autonomous vehicles (CAV). How fast the evolution of these two areas will go head-in-head is of great importance, which is the focus of this paper. After a brief overview on technological evolution of V2X to CAV and 6G key technologies, this paper explores two complementary research directions, namely, 6G for CAVs versus CAVs for 6G. The former investigates how various 6G key enablers, such as THz, cell free communication and artificial intelligence (AI), can be utilized to provide CAV mission-critical services. The latter discusses how CAVs can facilitate effective deployment and operation of 6G systems. This paper attempts to investigate the interactions between the two technologies to spark more research efforts in these areas

Implementation of connected and autonomous vehicles in cities could have neutral effects on the total travel time costs: modeling and analysis for a circular city

Autonomous vehicles promise to revolutionize the automobile market, although their implementation could take several decades in which both types of cars will coexist on the streets. We formulate a model for a circular city based on continuous approximations, considering demand surfaces over the city. Numerical results from our model predict direct and indirect effects of connected and autonomous vehicles. Direct effects will be positive for our cities: (a) less street supply is needed to accommodate the traffic; (b) congestion levels decrease: travel costs may decrease by 30%. Some indirect effects will counterbalance these positive effects: (c) a decrease of 20% in the value of travel time can reduce the total cost by a third; (d) induced demand could be as high as 50%, bringing equivalent total costs in the future scenario; (e) the vehicle-kilometers traveled could also affect the future scenario; and (f) increases in city size and urban sprawl. As a conclusion, the implementation of autonomous vehicles could be neutral for the cities regarding travel time costs. City planning agencies still have to promote complementary modes such as active mobility (walking and bicycle), transit (public transportation), and shared mobility (shared autonomous vehicles and mobility as a service).Peer ReviewedPostprint (published version

Traffic Congestion Aware Route Assignment

Traffic congestion emerges when traffic load exceeds the available capacity of roads. It is challenging to prevent traffic congestion in current transportation systems where vehicles tend to follow the shortest/fastest path to their destinations without considering the potential congestions caused by the concentration of vehicles. With connected autonomous vehicles, the new generation of traffic management systems can optimize traffic by coordinating the routes of all vehicles. As the connected autonomous vehicles can adhere to the routes assigned to them, the traffic management system can predict the change of traffic flow with a high level of accuracy. Based on the accurate traffic prediction and traffic congestion models, routes can be allocated in such a way that helps mitigating traffic congestions effectively. In this regard, we propose a new route assignment algorithm for the era of connected autonomous vehicles. Results show that our algorithm outperforms several baseline methods for traffic congestion mitigation

An Agent-based Modelling Framework for Driving Policy Learning in Connected and Autonomous Vehicles

Due to the complexity of the natural world, a programmer cannot foresee all possible situations, a connected and autonomous vehicle (CAV) will face during its operation, and hence, CAVs will need to learn to make decisions autonomously. Due to the sensing of its surroundings and information exchanged with other vehicles and road infrastructure, a CAV will have access to large amounts of useful data. While different control algorithms have been proposed for CAVs, the benefits brought about by connectedness of autonomous vehicles to other vehicles and to the infrastructure, and its implications on policy learning has not been investigated in literature. This paper investigates a data driven driving policy learning framework through an agent-based modelling approaches. The contributions of the paper are two-fold. A dynamic programming framework is proposed for in-vehicle policy learning with and without connectivity to neighboring vehicles. The simulation results indicate that while a CAV can learn to make autonomous decisions, vehicle-to-vehicle (V2V) communication of information improves this capability. Furthermore, to overcome the limitations of sensing in a CAV, the paper proposes a novel concept for infrastructure-led policy learning and communication with autonomous vehicles. In infrastructure-led policy learning, road-side infrastructure senses and captures successful vehicle maneuvers and learns an optimal policy from those temporal sequences, and when a vehicle approaches the road-side unit, the policy is communicated to the CAV. Deep-imitation learning methodology is proposed to develop such an infrastructure-led policy learning framework

the use of smartphones to assess the feasibility of a cooperative intelligent transportation safety system based on surrogate measures of safety

Abstract The future of road transportation is going to be shaped by connectivity and autonomous driving. Connected and autonomous vehicles are expected to increase safety and reduce traffic congestion. Once all the vehicles are connected and geo-localized there might still be a need to integrate a different level of autonomous vehicles on the road: from the human driven vehicle to the fully autonomous vehicle. While surrogate safety measures have been extensively considered to estimate the risk of accidents due to improper driving, there has been no attempt to use them to help drivers achieve a better driving style. This paper presents an experimentation on the idea to warn drivers when they are driving in such a way (owing to their interactions with other vehicles) that could potentially lead to an accident. In the proposed system the driver is warned of the risk of collision by the combined use of localization (GPS) gathered information and the application of road safety indicators such as Deceleration Rate to Avoid a Crash, Time To Collision and others. The experimentation involving car-following vehicles showed the feasibility, with existing technologies, of using surrogate measures of safety to assist the driver in keeping a better driving trajectory. Once connected vehicles are introduced on the market, the presented results can be a base to develop commercial smartphone applications that will allow users of "not connected" old vehicles to also take advantage of real time driving assistance for a safer use of the road