On Urban Traffic Flow Benefits of Connected and Automated Vehicles

Automated Vehicles are an integral part of Intelligent Transportation Systems (ITSs) and are expected to play a crucial role in the future mobility services. This paper investigates two classes of self-driving vehicles: (i) Level 4&5 Automated Vehicles (AVs) that rely solely on their on-board sensors for environmental perception tasks, and (ii) Connected and Automated Vehicles (CAVs), leveraging connectivity to further enhance perception via driving intention and sensor information sharing. Our investigation considers and quantifies the impact of each vehicle group in large urban road networks in Europe and in the USA. The key performance metrics are the traffic congestion, average speed and average trip time. Specifically, the numerical studies show that the traffic congestion can be reduced by up to a factor of four, while the average flow speeds of CAV group remains closer to the speed limits and can be up to 300% greater than the human-driven vehicles. Finally, traffic situations are also studied, indicating that even a small market penetration of CAVs will have a substantial net positive effect on the traffic flows.Comment: Accepted to IEEE VTC-Spring 2020, Antwerp, Belgiu

Assessing travel time savings and user benefits of automated driving – A case study for a commuting relation

Combining cooperative vehicle driving behavior of Connected and Automated Vehicles with supporting information infrastructure, is expected to increase the capacity of roadway infrastructure, which in turn results in travel time savings and user benefits. Automated driving also relieves the driver from steering the car, allowing to conduct other activities during the trip, which is likely to generate further user benefits. In order to assess the magnitude of automated driving on travel time-related user benefits, a typical commuting relation is analyzed, considering three route options as well as level 4 and 5 vehicle automation. The impacts on travel times are estimated by microscopic traffic flow simulations. The simulations reveal that around 27% of the travel time can be saved on a commuting relation due to road automation according to level 5. For level 4 vehicles the travel time savings amount to up to 20%. User benefits that accrue from time savings and the passenger\u27s option of using travelling time for activities other than conducting the car, are expected at a relevant magnitude. Even under consideration of higher operating costs of an automated car, significant user benefits accrue: 1,310–2,240 € p.a. for level 4 and 2,770–3,440 € p.a. for level 5 vehicles during a passenger car\u27s typical depreciation period. Thus, automated driving will decrease the commuters\u27 generalized user costs for individual motorized mobility, which is likely to enhance the urban hinterland\u27s attractiveness as residential area. This pattern and inherent second-order effects pose challenges for transport, land use and urban planners. Furthermore, it represents a challenge for transport research: to elaborate appropriate concepts that allow for exploiting the benefits of use of automated vehicles while countervailing undesirable socio-economic effects, as well as strains on the transport system and land use

Impacts of Connected and Automated Vehicles on Energy and Traffic Flow: Optimal Control Design and Verification Through Field Testing

This dissertation assesses eco-driving effectiveness in several key traffic scenarios that include passenger vehicle transportation in highway driving and urban driving that also includes interactions with traffic signals, as well as heavy-duty line-haul truck transportation in highway driving with significant road grade. These studies are accomplished through both traffic microsimulation that propagates individual vehicle interactions to synthesize large-scale traffic patterns that emerge from the eco-driving strategies, and through experimentation in which real prototyped connected and automated vehicles (CAVs) are utilized to directly measure energy benefits from the designed eco-driving control strategies. In particular, vehicle-in-the-loop is leveraged for the CAVs driven on a physical test track to interact with surrounding traffic that is virtually realized through said microsimulation software in real time. In doing so, model predictive control is designed and implemented to create performative eco-driving policies and to select vehicle lane, as well as enforce safety constraints while autonomously driving a real vehicle. Ultimately, eco-driving policies are both simulated and experimentally vetted in a variety of typical driving scenarios to show up to a 50% boost in fuel economy when switching to CAV drivers without compromising traffic flow. The first part of this dissertation specifically assesses energy efficiency of connected and automated passenger vehicles that exploit intention-sharing sourced from both neighboring vehicles in a highway scene and from traffic lights in an urban scene. Linear model predictive control is implemented for CAV motion planning, whereby chance constraints are introduced to balance between traffic compactness and safety, and integer decision variables are introduced for lane selection and collision avoidance in multi-lane environments. Validation results are shown from both large-scale microsimulation and through experimentation of real prototyped CAVs. The second part of this dissertation then assesses energy efficiency of automated line-haul trucks when tasked to aerodynamically platoon. Nonlinear model predictive control is implemented for motion planning, and simulation and experimentation are conducted for platooning verification under highway conditions with traffic. Then, interaction-aware and intention-sharing cooperative control is further introduced to eliminate experimentally measured platoon disengagements that occur on real highways when using only status-sharing control. Finally, the performance of automated drivers versus human drivers are compared in a point-to-point scenario to verify fundamental eco-driving impacts -- experimentally showing eco-driving to boost energy economy by 11% on average even in simple driving scenarios

Simulation of the Impact of Connected and Automated Vehicles at a Signalized Intersection

Intersections are locations with higher likelihood of crash occurences and sources of traffic congestion as they act as bottlenecks compared with other parts of the roadway networks. Consequently, connected and automated vehicles (CAVs) can help to improve the efficiency of the roadways by reducing traffic congestion and traffic delays. Since CAVs are expected to take control from drivers (human control) in making many important decisions, thus they are expected to minimize driver (human) errors in driving tasks. Therefore, CAVs potential benefits of eliminating driver error include an increase in safety (crash reduction), smooth vehicle flow to reduce emissions, and reduce congestion in all roadway networks. Since CAV implementations are currently in early stages, researchers have found that the use of traffic modeling and simulation can assist decision makers by quantifying the impact of increasing levels of CAVs, helping to identify the effect this will have on future transportation facilities. The main objective of the current study was to simulate the potential impacts CAVs may have on traffic flow and delay at a typical urban signalized intersection. Essentially, to use a microscopic traffic simulation software to test future CAV technology within a virtual environment, by testing different levels of CAVs with their associated behaviors across several scenarios simulated. This study tested and simulated the impact of CAVs compared with conventional vehicles at a signalized intersection. Specifically, I analyzed and compared the operations of the signalized intersection when there are only conventional vehicles, conventional vehicles mixed with CAVs, and when there are only CAVs

The State-of-the-art of Coordinated Ramp Control with Mixed Traffic Conditions

Ramp metering, a traditional traffic control strategy for conventional vehicles, has been widely deployed around the world since the 1960s. On the other hand, the last decade has witnessed significant advances in connected and automated vehicle (CAV) technology and its great potential for improving safety, mobility and environmental sustainability. Therefore, a large amount of research has been conducted on cooperative ramp merging for CAVs only. However, it is expected that the phase of mixed traffic, namely the coexistence of both human-driven vehicles and CAVs, would last for a long time. Since there is little research on the system-wide ramp control with mixed traffic conditions, the paper aims to close this gap by proposing an innovative system architecture and reviewing the state-of-the-art studies on the key components of the proposed system. These components include traffic state estimation, ramp metering, driving behavior modeling, and coordination of CAVs. All reviewed literature plot an extensive landscape for the proposed system-wide coordinated ramp control with mixed traffic conditions.Comment: 8 pages, 1 figure, IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE - ITSC 201

Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network

Accurate lane localization and lane change detection are crucial in advanced driver assistance systems and autonomous driving systems for safer and more efficient trajectory planning. Conventional localization devices such as Global Positioning System only provide road-level resolution for car navigation, which is incompetent to assist in lane-level decision making. The state of art technique for lane localization is to use Light Detection and Ranging sensors to correct the global localization error and achieve centimeter-level accuracy, but the real-time implementation and popularization for LiDAR is still limited by its computational burden and current cost. As a cost-effective alternative, vision-based lane change detection has been highly regarded for affordable autonomous vehicles to support lane-level localization. A deep learning-based computer vision system is developed to detect the lane change behavior using the images captured by a front-view camera mounted on the vehicle and data from the inertial measurement unit for highway driving. Testing results on real-world driving data have shown that the proposed method is robust with real-time working ability and could achieve around 87% lane change detection accuracy. Compared to the average human reaction to visual stimuli, the proposed computer vision system works 9 times faster, which makes it capable of helping make life-saving decisions in time