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The purpose of this paper is to develop a model for autonomous cars to establish trusted parties by combining distributed ledgers and self-driving cars in the traffic to provide single version of the truth and thus build public trust.

The model, which the authors call Witness of Things, is based on keeping decision logs of autonomous vehicles in distributed ledgers through the use of vehicular networks and vehicle-to-vehicle/vehicle-to-infrastructure (or vice versa) communications. The model provides a single version of the truth and thus helps enable the autonomous vehicle industry, related organizations and governmental institutions to discover the true causes of road accidents and their consequences in investigations.

In this paper, the authors explored one of the potential effects of blockchain protocol on autonomous vehicles. The framework provides a solution for operating autonomous cars in an untrusted environment without needing a central authority. The model can also be generalized and applied to other intelligent unmanned systems.

This study proposes a blockchain protocol-based record-keeping model for autonomous cars to establish trusted parties in the traffic and protect single version of the truth.

Software-define vehicular networks (SDVN) assure the direct programmability for controlling the vehicles with improved accuracy and flexibility. In this research, the resource allocation strategy is focused on which the seek-and-destroy algorithm is implemented in the controller in such a way that an effective allocation of the resources is done based on the multi-objective function.

The purpose of this study is focuses on the resource allocation algorithm for the SDVN with the security analysis to analyse the effect of the attacks in the network. The genuine nodes in the network are granted access to the communication in the network, for which the factors such as trust, throughput, delay and packet delivery ratio are used and the algorithm used is Seek-and-Destroy optimization. Moreover, the optimal resource allocation is done using the same optimization in such a way that the network lifetime is extended.

The security analysis is undergoing in the research using the simulation of the attackers such as selective forwarding attacks, replay attacks, Sybil attacks and wormhole attacks that reveal that the replay attacks and the Sybil attacks are dangerous attacks and in future, there is a requirement for the security model, which ensures the protection against these attacks such that the network lifetime is extended for a prolonged communication. The achievement of the proposed method in the absence of the attacks is 84.8513% for the remaining nodal energy, 95.0535% for packet delivery ratio (PDR), 279.258 ms for transmission delay and 28.9572 kbps for throughput.

The seek-and-destroy algorithm is one of the swarm intelligence-based optimization designed based on the characteristics of the scroungers and defenders, which is completely novel in the area of optimizations. The diversification and intensification of the algorithm are perfectly balanced, leading to good convergence rates.

The aim of this paper is to identify promoting and restraining factors of a novel mobile service in the pedestrian safety area.

This paper uses the case study methodology that focus on analyzing a specific case of mobile safety services in depth. A case study is especially suitable for an emerging case, such as pedestrian safety, where the aim is to identify relevant influencing factors of the particular case and not to generalize the findings. To gather data for case study analysis, several expert interviews were performed. Because they provided a large volume of data, the Service, Technology, Organization, and Finance business model framework was used as a way of structuring the analysis.

The main restraining factors are end-user value proposition, battery life, accuracy of GPS positioning and the revenue model. However, the service could improve traffic safety considerably and it should be introduced first locally in places, where many accidents take place. There is a great interest on driver data, which could be the main advantage for this service in the future. Integration to navigation products would complement the service significantly.

Current traffic safety-related literature covers mainly technical issues, and there are only few papers related to business model issues on that particular service. Observations of the various factors affecting the related evolution at an early phase of the life-cycle support further service design process.

The purpose of this study is to explore the influence of the electronic throttle (ET) dynamics and the average speed of multiple preceding vehicles on the stability of traffic flow.

An extended car-following model integrating the ET dynamics and the average speed of multiple preceding vehicles is presented in this paper. The novel model’s stability conditions are obtained by using the thought of control theory, and the modified Korteweg–de Vries equation is inferred in terms of the nonlinear analysis method. In addition, some simulation experiments are implemented to explore the properties of traffic flow, and the results of these experiments confirm the correctness of theoretical analysis.

In view of the results of theoretical analysis and numerical simulation, traffic flow will become more stable when the average speed and ET dynamics of multiple preceding vehicles are considered, and the stability of traffic flow will also be enhanced by increasing the number of preceding vehicles considered.

This study leaves the factors such as the mixed traffic flow, the multilane and so on out of account in real road environment, which more or less influences the traffic flow’s stability, so the real traffic environment is not fully reflected.

There is little research integrating ET dynamics and the average velocity of multiple preceding vehicles to study the properties of traffic flow. The enhanced model constructed in this study can better reflect the real traffic, which can also give some theoretical reference for the development of connected and autonomous vehicles.

In an intelligent transportation system (for short, ITS) environment, a vehicle’s motion is affected by the information in a large scale. The purpose of this paper is to study the integration effect of multiple vehicles’ delayed velocities on traffic flow.

This paper constructed a new car-following model to study the integration effect of multiple vehicles’ delayed velocities on traffic flow. The new model is analyzed by linear and nonlinear perturbation method theoretically and also verified by simulation.

It is found out that the integration of preceding vehicles’ delayed velocities affect the stability of traffic flow importantly, and three preceding vehicles’ delayed velocities information should be considered in real traffic.

The new car-following model by considering the integration effect of multiple vehicles’ delayed velocities is firstly proposed in this paper. The research result shows that three preceding vehicles’ delayed velocities information is the best choice to stabilizing traffic flow.

This study aims to propose a centralized optimal control model for automated left-turn platoon at contraflow left-turn lane (CLL) intersections.

The lateral lane change control and the longitudinal acceleration in the control horizon are optimized simultaneously with the objective of maximizing traffic efficiency and smoothness. The proposed model is cast into a mixed-integer linear programming problem and then solved by the branch-and-bound technique.

The proposed model has a promising control effect under different geometric controlled conditions. Moreover, the proposed model performs robustly under various safety time headways, lengths of the CLL and green times of the main signal.

This study proposed a centralized optimal control model for automated left-turn platoon at CLL intersections. The lateral lane change control and the longitudinal acceleration in the control horizon are optimized simultaneously with the objective of maximizing traffic efficiency and smoothness

Individuals’ driving behavior data are becoming available widely through Global Positioning System devices and on-board diagnostic systems. The incoming data can be sampled at rates ranging from one Hertz (or even lower) to hundreds of Hertz. Failing to capture substantial changes in vehicle movements over time by “undersampling” can cause loss of information and misinterpretations of the data, but “oversampling” can waste storage and processing resources. The purpose of this study is to empirically explore how micro-driving decisions to maintain speed, accelerate or decelerate, can be best captured, without substantial loss of information.

This study creates a set of indicators to quantify the magnitude of information loss (MIL). Each indicator is calculated as a percentage to index the extent of information loss (EIL) in different situations. An overall information loss index named EIL is created to combine the MIL indicators. Data from a driving simulator study collected at 20 Hertz are analyzed (N = 718,481 data points from 35,924 s of driving tests). The study quantifies the relationship between information loss indicators and sampling rates.

The results show that marginally more information is lost as data are sampled down from 20 to 0.5 Hz, but the relationship is not linear. With four indicators of MILs, the overall EIL is 3.85 per cent for 1-Hz sampling rate driving behavior data. If sampling rates are higher than 2 Hz, all MILs are under 5 per cent for importation loss.

This study contributes by developing a framework for quantifying the relationship between sampling rates, and information loss and depending on the objective of their study, researchers can choose the appropriate sampling rate necessary to get the right amount of accuracy.