Connected Vehicles: V2V and V2I Road Weather and Traffic Communication Using Cellular Technologies

Communication Delay Outlier Detection and Compensation for Teleoperation Using Stochastic State Estimation

There have been numerous studies attempting to overcome the limitations of current autonomous driving technologies. However, there is no doubt that it is challenging to promise integrity of safety regarding urban driving scenarios and dynamic driving environments. Among the reported countermeasures to supplement the uncertain behavior of autonomous vehicles, teleoperation of the vehicle has been introduced to deal with the disengagement of autonomous driving. However, teleoperation can lead the vehicle to unforeseen and hazardous situations from the viewpoint of wireless communication stability. In particular, communication delay outliers that severely deviate from the passive communication delay should be highlighted because they could hamper the cognition of the circumstances monitored by the teleoperator, or the control signal could be contaminated regardless of the teleoperator's intention. In this study, communication delay outliers were detected and classified based on the stochastic approach (passive delays and outliers were estimated as 98.67% and 1.33%, respectively). Results indicate that communication delay outliers can be automatically detected, independently of the real-time quality of wireless communication stability. Moreover, the proposed framework demonstrates resilience against outliers, thereby mitigating potential performance degradation.

Review of Integrated Chassis Control Techniques for Automated Ground Vehicles

Integrated chassis control systems represent a significant advancement in the dynamics of ground vehicles, aimed at enhancing overall performance, comfort, handling, and stability. As vehicles transition from internal combustion to electric platforms, integrated chassis control systems have evolved to meet the demands of electrification and automation. This paper analyses the overall control structure of automated vehicles with integrated chassis control systems. Integration of longitudinal, lateral, and vertical systems presents complexities due to the overlapping control regions of various subsystems. The presented methodology includes a comprehensive examination of state-of-the-art technologies, focusing on algorithms to manage control actions and prevent interference between subsystems. The results underscore the importance of control allocation to exploit the additional degrees of freedom offered by over-actuated systems. This paper systematically overviews the various control methods applied in integrated chassis control and path tracking. This includes a detailed examination of perception and decision-making, parameter estimation techniques, reference generation strategies, and the hierarchy of controllers, encompassing high-level, middle-level, and low-level control components. By offering this systematic overview, this paper aims to facilitate a deeper understanding of the diverse control methods employed in automated driving with integrated chassis control, providing insights into their applications, strengths, and limitations.

Universal Learning Approach of an Intelligent Algorithm for Non-GNSS Assisted Beamsteering in V2I Systems

In intelligent transportation systems, an important task is to provide a highly efficient communication channel between vehicles and other infrastructure objects that meets energy efficiency requirements and involves low time delays. The paper presents a method for generating synthetic data of the “vehicle-to-infrastructure” system, capable of simulating many scenarios of traffic situations to increase the generalizing ability of an intelligent beamsteering algorithm. The beamsteering algorithm is based on gradient boosting and is designed to connect and track vehicles with minimal delays without relying on GNSS coordinates. The predictors for the applied machine learning algorithm were: the relief, vehicle type, direction of movement and speed, timestamps, and the received signal power level. The generated dataset included the traffic model based on the Lighthill–Whitham–Richards macroscopic model and SUMO software package simulations. Simulation results showed 94% accuracy in correctly identified positions for the main lobe according to vehicle behavior.

Simulation-Based Analysis of "What-If" Scenarios with Connected and Automated Vehicles Navigating Roundabouts

Despite the potential of connected and automated vehicles (CAVs), there are still many open questions on how road capacity can be influenced and what methods can be used to assess its expected benefits in the progressive transition towards fully cooperative driving. This paper contributes to a better understanding of the benefits of CAV technologies by investigating mobility-related issues of automated vehicles operating with a cooperative adaptive cruise control system on roundabout efficiency using microscopic traffic simulation. The availability of the adjustment factors for CAVs provided by the 2022 Highway Capacity Manual allowed to adjust the entry capacity equations to reflect the presence of CAVs on roundabouts. Two mechanisms of entry maneuver based on the entry lane type were examined to compare the capacity target values with the simulated capacities. The microscopic traffic simulator Aimsun Next has been of great help in building the "what-if" traffic scenarios that we analysed to endorse hypothesis on the model parameters which affect the CAVs' capabilities to increase roundabouts' throughput. The results highlighted that the increasing penetration rates of CAVs have greater impacts on the operational performances of roundabouts, and provided a synthetic insight to assess the potential benefits of CAVs from an efficiency perspective.