Nowadays, with the advancement of technology, world is trending toward high mobility and dynamics. In this context, intersection management (IM) as one of the most crucial elements of the transportation sector demands high attention. Today, road entities including infrastructures, vulnerable road users (VRUs) such as motorcycles, moped, scooters, pedestrians, bicycles, and other types of vehicles such as trucks, buses, cars, emergency vehicles, and railway vehicles like trains or trams are able to communicate cooperatively using vehicle-to-everything (V2X) communications and provide traffic safety, efficiency, infotainment and ecological improvements. In this paper, we take into account different types of intersections in terms of signalized, semi-autonomous (hybrid) and autonomous intersections and conduct a comprehensive survey on various intersection management methods for heterogeneous connected vehicles (CVs). We consider heterogeneous classes of vehicles such as road and rail vehicles as well as VRUs including bicycles, scooters and motorcycles. All kinds of intersection goals, modeling, coordination architectures, scheduling policies are thoroughly discussed. Signalized and semi-autonomous intersections are assessed with respect to these parameters. We especially focus on autonomous intersection management (AIM) and categorize this section based on four major goals involving safety, efficiency, infotainment and environment. Each intersection goal provides an in-depth investigation on the corresponding literature from the aforementioned perspectives. Moreover, robustness and resiliency of IM are explored from diverse points of view encompassing sensors, information management and sharing, planning universal scheme, heterogeneous collaboration, vehicle classification, quality measurement, external factors, intersection types, localization faults, communication anomalies and channel optimization, synchronization, vehicle dynamics and model mismatch, model uncertainties, recovery, security and privacy. 

/pdf/a-comprehensive-survey-on-cooperative-intersection-2v6paaak.pdf

A Comprehensive Survey on Cooperative Intersection Management for Heterogeneous Connected Vehicles

Sooner than expected, roads will be populated with a plethora of connected and autonomous vehicles serving diverse mobility needs. Rather than being stand-alone, vehicles will be required to cooperate and coordinate with each other, referred to as cooperative driving executing the mobility tasks properly. Cooperative driving leverages Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication technologies aiming to carry out cooperative functionalities: (i) cooperative sensing and (ii) cooperative maneuvering. To better equip the readers with background knowledge on the topic, we firstly provide the detailed taxonomy section describing the underlying concepts and various aspects of cooperation in cooperative driving. In this survey, we review the current solution approaches in cooperation for autonomous vehicles, based on various cooperative driving applications, i.e., smart car parking, lane change and merge, intersection management, and platooning. The role and functionality of such cooperation become more crucial in platooning use-cases, which is why we also focus on providing more details of platooning use-cases and focus on one of the challenges, electing a leader in high-level platooning. Following, we highlight a crucial range of research gaps and open challenges that need to be addressed before cooperative autonomous vehicles hit the roads. We believe that this survey will assist the researchers in better understanding vehicular cooperation, its various scenarios, solution approaches, and challenges.

https://www.mdpi.com/1424-8220/21/11/3783/pdf

Collaborative Autonomous Driving—A Survey of Solution Approaches and Future Challenges

Due to the complex and dynamic character of intersection scenarios, the autonomous driving strategy at intersections has been a difficult problem and a hot point in the research of intelligent transportation systems in recent years. This paper gives a brief summary of state-of-the-art autonomous driving strategies at intersections. Firstly, we enumerate and analyze common types of intersection scenarios, corresponding simulation platforms, as well as related datasets. Secondly, by reviewing previous studies, we have summarized characteristics of existing autonomous driving strategies and classified them into several categories. Finally, we point out problems of the existing autonomous driving strategies and put forward several valuable research outlooks.

Autonomous Driving Strategies at Intersections: Scenarios, State-of-the-Art, and Future Outlooks

Traffic oscillation, a traffic phenomenon that generates many carbon emissions, originates from the amplification of traffic disturbance. The connected and automated vehicle (CAV) technology may help to weaken oscillations and reduce carbon emissions from mixed traffic. This paper explores the effects of various characteristics of disturbances and mixed platoons on CO2 emissions under different disturbance types. The results indicate that the addition of even only one CAV reduces carbon emissions under some disturbances. The disturbance types that make mixed traffic increase carbon emissions have also been identified. Diverse disturbance characteristics play different essential roles in ecological impacts of CAVs. The platoon characteristics such as CAV's position in a platoon and the heterogeneity of human factors, affect the extent of carbon emission reduction. Based on these findings, this paper proposes optimization recommendations for CAV cooperative eco-driving and green traffic management. 

Carbon Emission Impacts of Longitudinal Disturbance on Low-penetration Connected Automated Vehicle Environments

Energy efficiency is an important research topic in rail transit operations. Energy-efficient vertical alignment is a practical consideration during the line design process. Proper vertical...

Energy-Efficient Rail Transit Vertical Alignment Optimization: Gaussian Pseudospectral Method

In the last decade, research studies on parking planning mainly focused on path planning rather than trajectory planning. The results of trajectory planning are more instructive for a practical parking process. Therefore, this paper proposes a trajectory planning method in which the optimal autonomous valet parking (AVP) trajectory is obtained by solving an optimal control problem. Additionally, a vehicle kinematics model is established with the consideration of dynamic obstacle avoidance and terminal constraints. Then the parking trajectory planning problem is modeled as an optimal control problem, while the parking time and driving distance are set as the cost function. The homotopic method is used for the expansion of obstacle boundaries, and the Gauss pseudospectral method (GPM) is utilized to discretize this optimal control problem into a nonlinear programming (NLP) problem. In order to solve this NLP problem, sequential quadratic programming is applied. Considering that the GPM is insensitive to the initial guess, an online calculation method of vertical parking trajectory is proposed. In this approach, the offline vertical parking trajectory, which is calculated and stored in advance, is taken as the initial guess of the online calculation. The selection of an appropriate initial guess is based on the actual starting position of parking. A small parking lot is selected as the verification scenario of the AVP. In the validation of the algorithm, the parking trajectory planning is divided into two phases, which are simulated and analyzed. Simulation results show that the proposed algorithm is efficient in solving a parking trajectory planning problem. The online calculation time of the vertical parking trajectory is less than 2 s, which meets the real-time requirement.

https://www.mdpi.com/1424-8220/20/22/6435/pdf

A Trajectory Planning Method for Autonomous Valet Parking via Solving an Optimal Control Problem.

In recent years, eco-driving for hybrid electric vehicles (HEVs) has been studied with the emerging connected and automated vehicle (CAV) technologies to improve the mobility, fuel economy and safety of HEVs. This paper develops a hierarchical eco-driving control strategy for HEV platoons consisting of CAVs and human-driven vehicles (HDVs) to improve fuel economy at signalized intersections. For each platoon, the speed trajectories of CAVs are optimized in the upper layer controller using model predictive control (MPC) to minimize the total fuel consumption of the whole platoon. For each HEV, the optimal power split between the engine and the battery is obtained in the lower layer controller using adaptive equivalent consumption minimization strategy (A-ECMS). The time-varying powertrain efficiencies of HEVs are explicitly considered in the speed trajectory optimization. At last, simulation studies are conducted using MATLAB and VISSIM to evaluate the performances of the strategy in mixed traffic scenarios and different CAV penetration rates. Simulation results indicate that compared with the single vehicle control strategy, the proposed strategy can improve the average fuel economy by up to 8.34% and considering the time-varying powertrain efficiencies of HEVs in the optimization can further reduce the fuel consumption by up to 1.23%. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/itr2.12325

Hierarchical eco‐driving control strategy for hybrid electric vehicle platoon at signalized intersections under partially connected and automated vehicle environment

This study presents an optimal control strategy for connected and automated vehicles (CAVs) at unsignalized intersections. A control framework with task-decomposition is designed for the near-intersection area. Firstly, the continuous traffic flow is discretized with a proposed intersection area-division scheme. Then, vehicles in different lanes are regrouped based on longitudinal positions to establish multivehicle cooperative optimal control problems of limited vehicle number. The expected optimal trajectory in the merging zone is obtained through offline simulation, which simplifies the constraints of the optimal control problem and improves the real-time performance of the online trajectory solving. Finally, an ideal capacity model is proposed based on the transit time window of vehicle, so that the capacity of a single lane is increased to 2400veh/h. The simulation results demonstrate that the proposed strategy ensures the driving safety at the unsignalized intersection, and this strategy efficiently reduces planning time for vehicle trajectories.

Optimal Control of Connected and Automated Vehicles at Unsignalized Intersections: Discrete and Regroup

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Papers

A Trajectory Planning Method for Autonomous Valet Parking via Solving an Optimal Control Problem.

Hierarchical eco‐driving control strategy for hybrid electric vehicle platoon at signalized intersections under partially connected and automated vehicle environment

Optimal Control of Connected and Automated Vehicles at Unsignalized Intersections: Discrete and Regroup