Cooperative Traffic Control Solution for Vehicle Transition from Autonomous to Manual Mode exploiting Cellular Vehicle-to-Everything (C-V2X) Technology

Nowadays, automated vehicles represent a promising technology to face the stringent requirements for safety and traffic efficiency in the automotive environment. Driving responsibilities will be gradually addressed to the machine, and the role of human pilots will be progressively reduced to passengers. The interaction between passengers and the automated system will create different risks that have not been considered in the past. In particular, the transition between autonomous and manual mode is understood as a risky situation. During the transition, the driver manifests driving irregularities and loss of situation awareness that may endanger himself and other participants on the road. Hence, the vehicle transitioning needs a higher quantity of space around it to be considered safe. However, no effective solution has been developed yet. This thesis aims to design a cooperative traffic control solution that will manage the movements of the group of vehicles to increase the free space around the one transitioning. It will exploit another tool that will play a fundamental role in the future of the automotive industry: connected vehicles technology. C-V2X technology will create a medium for vehicles to exchange information and cooperate. A controller managing the cooperation between vehicles has been developed to help a smooth and safe vehicle repositioning. The controller will be positioned in a centralized computing facility and it will communicate with all the vehicles. The controller defines rules to move vehicles together and enlarge the free space around the vehicle transitioning without collisions. The rules are modeled by a spring-mass-damper system, that can be exploited to control the longitudinal behavior of automated vehicles. In particular, the spring-mass-damper system can manage smooth migration between vehicle dispositions without oscillations. A computer simulation is used to test the performance of the proposed traffic control system. The simulation environment is constituted by three main components: traffic flow, controller and communication network. It has been tested with the software VEINS, which provides interaction between a network simulator (OMNeT++) and a traffic simulator (SUMO). The traffic flow represents the interactions between vehicles. The controller analyzes the data and sends control messages to all vehicles. The communication network will share the data concerning vehicles’ position and speed and control messages. The proposed cooperative vehicle control system demonstrated to reduce the risks of the transition with the smooth motion of vehicles. The controller is able to achieve the safety requirements without reducing the level of comfortability of vehicles’ passengers

Adoption of vehicular ad hoc networking protocols by networked robots

This paper focuses on the utilization of wireless networking in the robotics domain. Many researchers have already equipped their robots with wireless communication capabilities, stimulated by the observation that multi-robot systems tend to have several advantages over their single-robot counterparts. Typically, this integration of wireless communication is tackled in a quite pragmatic manner, only a few authors presented novel Robotic Ad Hoc Network (RANET) protocols that were designed specifically with robotic use cases in mind. This is in sharp contrast with the domain of vehicular ad hoc networks (VANET). This observation is the starting point of this paper. If the results of previous efforts focusing on VANET protocols could be reused in the RANET domain, this could lead to rapid progress in the field of networked robots. To investigate this possibility, this paper provides a thorough overview of the related work in the domain of robotic and vehicular ad hoc networks. Based on this information, an exhaustive list of requirements is defined for both types. It is concluded that the most significant difference lies in the fact that VANET protocols are oriented towards low throughput messaging, while RANET protocols have to support high throughput media streaming as well. Although not always with equal importance, all other defined requirements are valid for both protocols. This leads to the conclusion that cross-fertilization between them is an appealing approach for future RANET research. To support such developments, this paper concludes with the definition of an appropriate working plan

Mind the Gap: Developments in Autonomous Driving Research and the Sustainability Challenge

Scientific knowledge on autonomous-driving technology is expanding at a faster-than-ever pace. As a result, the likelihood of incurring information overload is particularly notable for researchers, who can struggle to overcome the gap between information processing requirements and information processing capacity. We address this issue by adopting a multi-granulation approach to latent knowledge discovery and synthesis in large-scale research domains. The proposed methodology combines citation-based community detection methods and topic modeling techniques to give a concise but comprehensive overview of how the autonomous vehicle (AV) research field is conceptually structured. Thirteen core thematic areas are extracted and presented by mining the large data-rich environments resulting from 50 years of AV research. The analysis demonstrates that this research field is strongly oriented towards examining the technological developments needed to enable the widespread rollout of AVs, whereas it largely overlooks the wide-ranging sustainability implications of this sociotechnical transition. On account of these findings, we call for a broader engagement of AV researchers with the sustainability concept and we invite them to increase their commitment to conducting systematic investigations into the sustainability of AV deployment. Sustainability research is urgently required to produce an evidence-based understanding of what new sociotechnical arrangements are needed to ensure that the systemic technological change introduced by AV-based transport systems can fulfill societal functions while meeting the urgent need for more sustainable transport solutions

A self-learning intersection control system for connected and automated vehicles

This study proposes a Decentralized Sparse Coordination Learning System (DSCLS) based on Deep Reinforcement Learning (DRL) to control intersections under the Connected and Automated Vehicles (CAVs) environment. In this approach, roadway sections are divided into small areas; vehicles try to reserve their desired area ahead of time, based on having a common desired area with other CAVs; the vehicles would be in an independent or coordinated state. Individual CAVs are set accountable for decision-making at each step in both coordinated and independent states. In the training process, CAVs learn to minimize the overall delay at the intersection. Due to the chain impact of taking random actions in the training course, the trained model can deal with unprecedented volume circumstances, the main challenge in intersection management. Application of the model to a single-lane intersection with no turning movement as a proof-of-concept test reveals noticeable improvements in traffic measures compared to three other intersection control systems. A Spring Mass Damper (SMD) model is developed to control platooning behavior of CAVs. In the SMD model, each vehicle is assumed as a mass, coupled with its preceding vehicle with a spring and a damper. The spring constant and damper coefficient control the interaction between vehicles. Limitations on communication range and the number of vehicles in each platoon are applied in this model, and the SMD model controls intra-platoon and inter-platoon interactions. The simulation result for a regular highway section reveals that the proposed platooning algorithm increases the maximum throughput by 29% and 63% under 50% and 100% market penetration rate of CAVs. A merging section with different volume combinations on the main section and merging section and different market penetration rates of CAVs is also modeled to test inter-platoon spacing performance in accommodating merging vehicles. Noticeable travel time reduction is observed in both mainline and merging lanes and under all volume combinations in 80% and higher MPR of CAVs. For a more reliable assessment of the DSCLS, the model is applied to a more realistic intersection, including three approaching lanes in each direction and turning movements. The proposed algorithm decreases delay by 58%, 19%, and 13% in moderate, high, and extreme volume regimes, improving travel time accordingly. Comparison of safety measures reveals 28% improvement in Post Encroachment Time (PET) in the extreme volume regime and minor improvements in high and moderate volume regimes. Due to the limited acceleration and deceleration rates, the proposed model does not show a better performance in environmental measures, including fuel consumption and CO2 emission, compared to the conventional control systems. However, the DSCLS noticeably outperforms the other pixel-reservation counterpart control system, with limited acceleration and deceleration rates. The application of the model to a corridor of four interactions shows the same trends in traffic, safety, and environmental measures as the single intersection experiment. An automated intersection control system for platooning CAVs is developed by combining the two proposed models, which remarkably improves traffic and safety measures, specifically in extreme volume regimes compared to the regular DSCLS model

Modelling mixed traffic flow of autonomous vehicles and human-driven vehicles

Autonomous Vehicles (AVs) are bringing revolutionary opportunities and challenges to urban transport systems. They can reduce congestion, improve operational efficiency and liberate drivers from driving. Though AVs might bring attractive potential benefits, most benefits are evaluated at high AV penetration rates or an all-AV scenario. In practice, limited by price barriers, adoption rates and vehicle-renewal periods, AVs may not replace Human-Driven Vehicles (HDVs) to achieve a high penetration rate in a short time. It can be expected that the road network will operate with a mix of AVs and HDVs in the near to medium future. Therefore, there is a strong motivation to analyse the performance of road networks under mixed traffic conditions. The overall aim of this PhD research is to analyse mixed traffic flows of AVs and HDVs to help traffic managers and Local Authorities (LAs) improve the performance of urban traffic systems by right-of-way reallocation and dynamic traffic management. To achieve this aim, this PhD research is divided into four parts. Firstly, the impact of heterogeneity between AVs and HDVs on road capacity is investigated. A theoretical model is proposed to calculate the maximum capacity of heterogeneous traffic flow. Based on the theoretical model, it is shown that road capacity increases convexly with AV penetration rates. This finding provides a theoretical basis to support the hypothesis that right-of-way reallocation can increase road capacity under the mixed traffic flow. To cross-validate the above finding, different right-of-way reallocation strategies are evaluated on a two-lane road with SUMO simulation. Compared with a do-nothing scenario, the road capacity can be increased by approximately 11% with a proper RoW reallocation strategy at low or medium AV penetration rates. Secondly, whether CAVs can be used as mobile traffic controllers by adjusting their speed on a certain link is investigated. It is found that in some circumstances, system efficiency can be improved by CAVs adjusting their speed on a certain link to nudge the network towards the system optimum. According to a numerical analysis on the Braess network, total travel time can be reduced by 9.7% when CAVs actively slow down on a link. To take more realistic circumstances into account, a SUMO simulation case study is conducted, where HDVs only have partial knowledge about travel costs. The results of the simulation demonstrate that when CAVs are acting as mobile traffic controllers by actively reducing speed on a certain link, total travel time can be reduced by approximately 6.8% compared with the do-nothing scenario. Thirdly, whether travel efficiency can be improved with only a part of the vehicular flow cooperatively changing their routing under mixed conditions is investigated. It has been found that it is possible to use CAVs to influence HDVs’ day-to-day routing and push the network towards the system optimal distribution dynamically on a large network with multiple OD pairs. Taking non-linear cost-flow relationship and signal timing into account, an Optimal Routing and Signal Timing (ORST) control strategy is proposed for CAVs and tested in simulation. Compared with initial user equilibrium, total travel time can be reduced by approximately 7% when a portion of CAVs cooperatively charge their routing with the ORST control strategy at the 75% CAV penetration rate. This opens up possibilities, besides road pricing, to improve system efficiency by controlling routing and signal timing strategy for CAVs. Fourthly, whether additional travel efficiency can be achieved by jointly optimising routing and signal timing with information from CAVs is further investigated. Specifically, the impact of information levels on routing and signal timing efficiency has been investigated quantitatively. The results demonstrate that different levels of information will lead the road traffic system to reach different equilibrium points. Then the proposed ORST control strategy is compared with existing routing and signal timing strategies. The results present that ORST can reduce approximately 10% of the total travel time compared to user equilibrium. In addition, the proposed model has also been tested on a revised Nguyen-Dupuis network. At 25% CAV penetration rates, the proposed model can successfully reduce approximately 23% of total travel time. In summary, the mixed flow of AVs and HDVs is investigated in this PhD research. To increase the efficiency of urban traffic systems, novel strategies have been proposed and tested with numerical analysis and simulation, which provides inspirations and quantitative evidence for traffic managers and LAs to manage the mixed traffic flow efficiently.Open Acces