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The purpose of this paper is to present work which is a part of the Comprehensive Automation for Specialty Crops project (CASC). Desired trajectory tracking objective has been previously performed by using a non‐model based approach in this project. Long distance autonomous drive has been achieved; however the results haven't met the expectations of the project requirements. In order to provide these requirements, this study is conducted. In this study, long distance autonomous trajectory tracking for an orchard vehicle is studied. Besides longitudinal motion, lateral motion of the vehicle is also considered. The longitudinal and lateral errors are objected to keep into a region of less than 10 cm.

Car‐like robot kinematic modeling approach is used to create desired trajectory. In order to control longitudinal velocity and steering angle of the vehicle, a controller methodology is proposed. Stability of the controller proposed is shown by using Lyapunov stability approach.

The proposed model is adapted into a four‐wheeled autonomous orchard vehicle and tested in an experimental orchard for long distance autonomous drives. More than 15 km autonomous drive is successfully achieved and the details are presented in this paper.

In this study, long distance autonomous trajectory tracking for an orchard vehicle is focused. A model based control strategy, including the information about longitudinal and lateral motion of the vehicle, is constructed. A new approach to create steering angles for turning operations of the orchard vehicle is introduced. It is objected that the longitudinal and lateral errors should be less than 10 cm during the trajectory tracking task.

Nowadays, transportation authorities in various countries are in tension as to whether to invest in railroads or roads. There are arguments for each side, and in the end, each transportation authority reaches a kind of balance between the investments. This study aims to anticipate how autonomous vehicles will influence this decision.

The roads' capacity in the era of autonomous vehicles is assessed and research has concluded that the anticipated increase in road capacity will encourage transportation authorities to invest much more in roads than in railroads.

The appearance of the autonomous vehicles will significantly change the balance in favor of the roads, because the roads' capacity will be increased substantially so the roads will be able to accommodate many more vehicles.

Currently, autonomous vehicles are still very rare.

The impact of autonomous vehicles on the decision whether to build more roads is explained.

The study explained why the transportation authorities in the various countries will be more inclined to switch to road construction and why the transition to more roads and fewer railroads will likely be done gradually as more autonomous vehicles enter service.

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.

There is lack of knowledge about how the existing streets need to be redesigned and the infrastructural changes that need to be made to adopt autonomous vehicles. The purpose of this study is to investigate the infrastructure requirements of autonomous vehicles in terms of (1) lane widths, (2) parking spaces, (3) drop-off zones and (4) other facilities, followed by analyzing them and suggesting changes in the existing urban design of Msheireb Downtown Doha (MDD).

Mixed method of combining both qualitative (secondary research of analyzing the existing data about the urban design guidelines for an autonomous future, observations of the existing infrastructure) and quantitative methods (on-site measurements of pedestrian walkways and road lane widths) is used.

The outcome of the research consists of a series of major infrastructural changes with regard to lane widths, parking spaces, pick-up and drop-off zones and other facilities needed for the deployment of autonomous vehicles.

The results imply that Qatar can benefit by adopting the proposed urban design suggestions for the implementation of autonomous vehicles on the streets of MDD in particular, and smart cities of Qatar and the region in general.

The proposed changes can work as a reference and serve as a possible setting for addressing Autonomous Vehicle preparations in emerging cities.

The proposed urban design changes can be adapted for an autonomous future in emerging cities.

With the aid of naturalistic simulations, this paper aims to investigate human behavior during manual and autonomous driving modes in complex scenarios.

The simulation environment is established by integrating virtual reality interface with a micro-simulation model. In the simulation, the vehicle autonomy is developed by a framework that integrates artificial neural networks and genetic algorithms. Human-subject experiments are carried, and participants are asked to virtually sit in the developed autonomous vehicle (AV) that allows for both human driving and autopilot functions within a mixed traffic environment.

Not surprisingly, the inconsistency is identified between two driving modes, in which the AV’s driving maneuver causes the cognitive bias and makes participants feel unsafe. Even though only a shallow portion of the cases that the AV ended up with an accident during the testing stage, participants still frequently intervened during the AV operation. On a similar note, even though the statistical results reflect that the AV drives under perceived high-risk conditions, rarely an actual crash can happen. This suggests that the classic safety surrogate measurement, e.g. time-to-collision, may require adjustment for the mixed traffic flow.

Understanding the behavior of AVs and the behavioral difference between AVs and human drivers are important, where the developed platform is only the first effort to identify the critical scenarios where the AVs might fail to react.

This paper attempts to fill the existing research gap in preparing close-to-reality tools for AV experience and further understanding human behavior during high-level autonomous driving.

This work aims to systematically analyze the inconsistency in driving patterns between manual and autopilot modes in various driving scenarios (i.e. multiple scenes and various traffic conditions) to facilitate user acceptance of AV technology.

A close-to-reality tool for AV experience and AV-related behavioral study. A systematic analysis in relation to the inconsistency in driving patterns between manual and autonomous driving. A foundation for identifying the critical scenarios where the AVs might fail to react.

The purpose of this paper is to design a unified operational design domain (ODD) monitoring framework for mitigating Safety of the Intended Functionality (SOTIF) risks triggered by vehicles exceeding ODD boundaries in complex traffic scenarios.

A unified model of ODD monitoring is constructed, which consists of three modules: weather condition monitoring for unusual weather conditions, such as rain, snow and fog; vehicle behavior monitoring for abnormal vehicle behavior, such as traffic rule violations; and road condition monitoring for abnormal road conditions, such as road defects, unexpected obstacles and slippery roads. Additionally, the applications of the proposed unified ODD monitoring framework are demonstrated. The practicability and effectiveness of the proposed unified ODD monitoring framework for mitigating SOTIF risk are verified in the applications.

First, the application of weather condition monitoring demonstrates that the autonomous vehicle can make a safe decision based on the performance degradation of Lidar on rainy days using the proposed monitoring framework. Second, the application of vehicle behavior monitoring demonstrates that the autonomous vehicle can properly adhere to traffic rules using the proposed monitoring framework. Third, the application of road condition monitoring demonstrates that the proposed unified ODD monitoring framework enables the ego vehicle to successfully monitor and avoid road defects.

The value of this paper is that the proposed unified ODD monitoring framework establishes a new foundation for monitoring and mitigating SOTIF risks in complex traffic environments.

Autonomous vehicles rely on IoT-based technologies to take numerous decisions in real-time situations. However, added information from the sensor readings will burden the system and cause the sensors to produce inaccurate readings. To overcome these issues, this paper aims to focus on communication between sensors and autonomous vehicles for better decision-making in real-time. The system has unique features to detect the upcoming and ongoing vehicles automatically without intervention of humans in the system. It also predicts the type of vehicle and intimates the driver.

The system is designed using the ATmega 328 P and ESP 8266 chip. Information from ultrasonic and infrared sensors are analyzed and updated in the cloud server. The user can access all these real-time data at any point of time. The stored information in cloud servers is used for integrating artificial intelligence into the system.

The real-time sensor information is used to predict the surrounding environment and the system responds to the user according to the situation.

The system is implemented on embedded platform with IoT technology. The sensor information is updated to the cloud using the Blynk application for the user in real time.

The system is proposed for smart cities with IoT technology where the user and the system are aware of the surrounding environment. The system is mainly concerned with the accuracy of sensors and the distance between the vehicles in real-time environment.