Connected and Automated Vehicles (CAVs) are highly expected to improve traffic throughput and safety at road intersections, single-track lanes, and construction zones. However, multiple CAVs can block each other and create a mutual deadlock around these road segments (i) when vehicle systems have a failure, such as a communication failure, control failure, or localization failure and/or (ii) when vehicles use a long shared road segment. In this paper, we present an Autonomous Deadlock Detection and Recovery Protocol at Intersections for Automated Vehicles named A-DRIVE that is a decentralized and time-sensitive technique to improve traffic throughput and shorten worst-case recovery time. To enable the deadlock recovery with automated vehicles and with human-driven vehicles, A-DRIVE includes two components: V2V communication-based A-DRIVE and Local perception-based A-DRIVE. V2V communication-based A-DRIVE is designed for homogeneous traffic environments in which all the vehicles are connected and automated. Local perception-based A-DRIVE is for mixed traffic, where CAVs, non-connected automated vehicles, and human-driven vehicles co-exist and cooperate with one another. Since these two components are not exclusive, CAVs inclusively and seamlessly use them in practice. Finally, our simulation results show that A-DRIVE improves traffic throughput compared to a baseline protocol.

Emotions are essential for constructing social relationships between humans and interactive systems. Although emotional and empathetic dialogue generation methods have been proposed for dialogue systems, appropriate dialogue involves not only mirroring emotions and always being empathetic but also complex factors such as context. This paper proposes Emotion Regulation Chat (ER-Chat) as an end-to-end dialogue framework for emotion regulation. Emotion regulation is concerned with actions to approach appropriate emotional states. Learning appropriate emotion and intent when responding on the basis of the context of the dialogue enables the generation of more human-like dialogue. We conducted automatic and human evaluations to demonstrate the superiority of ER-Chat over the baseline system. The results show that inclusion of emotion and intent prediction mechanisms enable generation of dialogues with greater fluency, diversity, emotion awareness, and emotion appropriateness, which are greatly preferred by humans.

Autonomous driving systems are becoming increasingly feasible and highly expected to be the heart of intelligent transportation systems. To deploy the autonomous driving vehicles on public roads, one of the practical challenges might be safe cooperation and collaboration among multiple vehicles, in particular when conflicts arise on shared road segments, such as road intersections, merge points, construction zones, single-track lanes, and center turn lane. In the current traffic systems, human drivers navigate these regions using a combination of traffic rules, social norms, courtesy, hand signals, and common sense. In this paper, we identify and classify such Dynamic Intersections that might lead to vehicle accidents and/or deadlocks and that might appear almost anytime and anywhere on public roads. In addition, we present a cooperative dynamic intersection protocol that uses on-board perception systems and vehicular communications for peer-to-peer negotiation. Under our protocol, autonomous driving vehicles can create a vehicular communication-based traffic manager named Cyber Traffic Light when congestion arises. Cyber Traffic Light works as a self-organizing, self-planning, and self-optimizing traffic manager, and it allocates the green period for vehicles coming from the multiple directions. Finally, we showed that our decentralized protocol has much higher traffic throughput, compared to two simple protocols while guaranteeing road safety.

Connected and Automated Vehicles (CAVs) have real-time information from the surrounding environment by using local on-board sensors, V2X (Vehicle-to-Everything) communications, pre-loaded vehicle-specific lookup tables, and map database. CAVs are capable of improving energy efficiency by incorporating these information. In particular, Eco-Cruise and Eco-Lane Selection on highways and/or motorways have immense potential to save energy, because there are generally fewer traffic controllers and the vehicles keep moving in general. In this paper, we present a cooperative and energy-efficient lane-selection strategy named MultiCruise, where each CAV selects one among multiple candidate lanes that allows the most energy-efficient travel. MultiCruise incorporates an Eco-Cruise component to select the most energy-efficient lane. The Eco-Cruise component calculates the driving parameters and prospective energy consumption of the ego vehicle for each candidate lane, and the Eco-Lane Selection component uses these values. As a result, MultiCruise can account for multiple data sources, such as the road curvature and the surrounding vehicles' velocities and accelerations. The eco-autonomous driving strategy, MultiCruise, is tested, designed and verified by using a co-simulation test platform that includes autonomous driving software and realistic road networks to study the performance under realistic driving conditions. Our experimental evaluations show that our eco-autonomous Mul-tiCruise saves up to 8.5% fuel consumption.

Human-robot collaboration and cooperation are critical for Autonomous Mobile Robots (AMRs) in order to use them in indoor environments, such as offices, hospitals, libraries, schools, factories, and warehouses. Since a long transition period might be required to fully automate such facilities, we have to deploy AMRs while improving safety in the mixed environments of human and mobile robots. In addition, human behaviors in such environments might be difficult to predict. In this paper, we present a Digital Twin for Autonomous Mobile Robots system named DigiMobot to support, manage, monitor, and validate AMRs in indoor environments. First, DigiMobot can simulate human behaviors and robot movements to verify and validate AMRs to improve safety in a virtual world. Secondly, DigiMobot can monitor and manage AMRs in the physical world by collecting sensor data from each robot in real-time. Since DigiMobot enables us to test the robot systems in the virtual world, we can deploy and implement AMRs in each facility without any modifications. To show the feasibility of DigiMobot, we develop a software framework and two different types of autonomous mobile robots. Finally, we conduct real-world experiments in a warehouse located in Saitama, Japan, in which more than 400, 000 items are stored for commercial purposes.

In vehicular edge computing (VEC), offloading the tasks to the nearby resource-rich edge servers helps each vehicle enhance computational capabilities and improve in-vehicle applications' performance. However, the concentration of travel at specific spaces and times poses significant challenges on the load-balancing and scheduling of computation tasks at the edge servers. This paper studies a low-complexity dynamic online offloading strategy that efficiently reduces task delay and computing resource consumption in the multi-user, multiserver vehicular edge computing scenarios. Our design addresses issues of computation task placement and execution order of the tasks on each server. We use a realistic approach that vehicles generate tasks over time, and the set of the tasks is unknown in advance so that the offloading decisions are made in runtime. Extensive simulations are conducted on a real mobility trace of Luxembourg city, and the results show that the proposed algorithm effectively improves the offloading utility of the system.

Large-scale events attracting many participants might have a strong negative impact on productivity, mobility, comfort, and safety in a city. Within the few years, serious accidents led by congestion have occurred, especially during sports events, religious ceremonies, festivals, and so on. To alleviate these serious accidents, predicting the occurrence of a large-scale event is much significant. When we know an event occurrence in advance, some of those who are not interested in the event might change their plans and/or might take a detour to avoid to get involved in a heavy congestion. In this paper, we present an early event detection technique named BusBeat. BusBeat uses GPS trajectory data collected from periodic-cars that are vehicles periodically traveling on a pre-scheduled route with a pre-determined departure time, such as a transit bus, shuttle, garbage truck, or municipal patrol car. BusBeat interpolates the missing GPS data by using the features of the periodic-cars. In addition, BusBeat uses the network-based analysis with a Time-dependent Congestion Network (TCN) in order to detect geo-spatial events. BusBeat achieves early event detection without incurring any privacy invasion, by using the continuous trajectories of the periodic-cars that provide the real-time traffic flow and speed. Since traffic towards an event venue would be slow before the event starts, BusBeat detects the geo-spatial events before the attendees gather. We evaluate our BusBeat using over 7,000-bus data collected in Beijing for 5 months and compare with the check-in data collected from a social network service.

With advances in sensing, computing and communication technologies, Connected and Automated Vehicles (CAVs) are becoming feasible. The advent of CAVs presents new opportunities to improve the energy efficiency of individual vehicles. However, testing and verifying energy-efficient autonomous driving systems are difficult due to safety considerations and repeatability. In this paper, we present a co-simulation platform to develop and test novel vehicle eco-autonomous driving technologies named InfoRich, which incorporates the information from on-board sensors, V2X communications, and map database. The co-simulation platform includes eco-autonomous driving software, vehicle dynamics and powertrain (VD&PT) model, and a traffic environment simulator. Also, we utilize synthetic drive cycles derived from real-world driving data to test the strategies under realistic driving scenarios. To build road networks from the real-world driving data, we develop an Automated Parser and Calculator for Map/Scenario named AutoPASCAL. Overall, the simulation platform provides a realistic vehicle model, powertrain model, sensor model, traffic model, and road-network model to enable the evaluation of the energy efficiency of eco-autonomous driving.

Sensor-based perception on vehicles are becoming prevalent and important to enhance road safety. Autonomous driving systems use cameras, LiDAR and radar to detect surrounding objects, while human-driven vehicles use them to assist the driver. However, the environmental perception by individual vehicles has the limitations on coverage and/or detection accuracy. For example, a vehicle cannot detect objects occluded by other moving/static obstacles. In this paper, we present a cooperative perception scheme with deep reinforcement learning to enhance the detection accuracy for the surrounding objects. By using deep reinforcement learning to select the data to transmit, our scheme mitigates the network load in vehicular networks and enhances the communication reliability. To design, test and verify the practical and resource-efficient cooperative perception framework, we develop a Cooperative & Intelligent Vehicle Simulation (CIVS) Platform where we integrate three software components: a traffic simulator, a vehicle simulator, and an object classifier. The simulation platform constitutes a unified framework to evaluate a traffic model, vehicle model, communication model, and object classification model. Simulation results show that our scheme decreases packet loss and thereby increases the detection accuracy by up to 12%, compared to the baseline protocol.

Self-driving vehicles are expected to be at the core of future transportation systems. Over time, these vehicles might enhance traffic efficiency and safety, especially at road intersections. However, there will likely be a long transition period before human-driven vehicles will be completely replaced by automated vehicles. Intersection safety and efficiency might be barely improved if automated vehicles just follow the traffic light signals. In this paper, we present a decentralized intersection protocol named the Distributed Synchronous Intersection Protocol (DSIP) that is for mixed traffic environments, where human-driven and self-driving vehicles cooperate with one another in order to avoid vehicle collisions and possible deadlocks while improving traffic efficiency. In DSIP, all automated vehicles use both Vehicle-to-Vehicle (V2V) communications and traffic lights to traverse the intersection safely. On the other hand, human-driven vehicles simply follow the traffic lights just like they do today. Under the protocol, the automated vehicles synchronize when there are no human-driven vehicles around the intersection. In addition, Cooperative Perception is used to detect the presence of human-driven vehicles, where all automated and connected vehicles sense and share the presence of the human-driven vehicles at the intersection. Our simulation results show that DSIP increases the traffic throughput of the intersections compared to common signalized intersections and other V2V-based intersection protocols.

Intersection management is one of the main challenging issues in road safety because intersections are a leading cause of traffic congestion and accidents. In fact, more than 44% of all reported crashes in the U.S. occur around intersection areas, which, in turn, has led to 8,500 fatalities and approximately 1 million injuries every year. With vehicles expected to become self-driving, the question is whether high throughput can be obtained through intersections while keeping them safe. A spatio-temporal intersection protocol named the Ballroom Intersection Protocol (BRIP) [8] was recently proposed in the literature to address this situation. Under this protocol, automated and connected vehicles arrive at and go through an intersection in a cooperative fashion with no vehicle needing to stop, while maximizing the intersection throughput. Though no vehicles run into one another under ideal environments with BRIP, vehicle accidents can occur when the self-driving vehicles have location errors and/or control system failure. In this article, we present a safe and practical intersection protocol named the Configurable Synchronous Intersection Protocol (CSIP). CSIP is a more general and resilient version of BRIP. CSIP utilizes a certain inter-vehicular distance to meet safety requirements in the presence of GPS inaccuracies and control failure. The inter-vehicular distances under CSIP are much more acceptable and comfortable to human passengers due to longer inter-vehicular distances that do not cause fear. With CSIP, the inter-vehicular distances can also be changed at each intersection to account for different traffic volumes, GPS accuracy levels, and geographical layout of intersections. Our simulation results show that CSIP never leads to traffic accidents even when the system has typical location errors, and that CSIP increases the traffic throughput of the intersections compared to common signalized intersections.

Due to the advent of active safety features and automated driving capabilities, the scope and complexity of embedded computing systems within automobiles continue to increase. Moreover, with the introduction of communication technologies like dedicated short range communications (DSRC), autonomous vehicles can also communicate with each other, pedestrians and the infrastructure. As the number of applications and levels of autonomy within such connected and autonomous vehicles (CAVs) increase the inherent safety-critical nature of CAVs imposes strict requirements in terms of testing and verification of system correctness. Hence, appropriate tools and methodologies are to manage these increasing complexity and testing requirements. In this paper, we present a standard reference architecture for CAVs and the tools and methodologies we use to model, design, develop and test systems to realize CAV applications.

The analysis and study of the relationship between a geo-spatial event and human mobility in an urban area is very significant for improving productivity, mobility, and safety. In particular, in order to alleviate serious road congestions, traffic jams, and stampedes, it is essential to predict and be informed about the occurrence of an event as soon as possible. When we know an event occurrence in advance, some of those who are not interested in the event might change their plans and/or might take a detour to avoid to get involved in a heavy congestion. In this context, this paper presents an early event detection technique using GPS trajectories collected from periodic-cars, which are vehicles periodically traveling on a pre-scheduled route with a pre-determined departure time, such as a transit bus, shuttle, garbage truck, or municipal patrol car. Using these trajectories, which provide the real-time and continuous traffic flow and speed, our technique detects large-scale events in advance, without incurring any privacy invasion. The behavior of periodic-cars shows a certain sign of a large-scale event before attendees gather around a venue because traffic can be slowed around the venue before the event occurrence. We evaluated our method using over 7,000-bus data from January to May in 2015 in Beijing, which we compared with the check-in data collected from a social network service.

Road intersections represent a leading cause of traffic congestion and accidents. In fact, more than 44% of all reported crashes in the U.S. occur within intersection areas, which, in turn, lead to 8,500 fatalities and approximately 1 million injuries every year. The expected advent of self-driving vehicles raises the question of how automated and connected vehicles can be used to improve throughput at intersections while keeping them safe. A spatio-temporal intersection protocol named the Ballroom Intersection Protocol (BRIP) [5] was recently proposed in the literature to address this situation. Under this protocol, automated vehicles arrive at and go through an intersection in a cooperative fashion with no vehicle needing to stop, while maximizing the intersection throughput. Though no vehicles run into one another under ideal environments with BRIP, vehicle accidents can occur when the self-driving vehicles have location errors. In this paper, we present a safe and practical intersection protocol named the Configurable Synchronous Intersection Protocol (CSIP) that is a more general and resilient version of BRIP. CSIP utilizes a certain inter-vehicle distance to meet safety requirements against GPS inaccuracy and control failure. In addition, the inter-vehicle distances under CSIP are much more acceptable to human passengers due to longer inter-vehicle distances that do not cause fear. With CSIP, the inter-vehicle distances can also be changed at each intersection to account for different traffic volumes and GPS inaccuracies. Our simulation results show that CSIP never leads to traffic accidents even when the system has typical location errors, and that CSIP increases the traffic throughput of the intersections compared to common signalized intersections.

Privacy issue has become serious in the area of mobile participatory sensing, smart grids, location-based services, and intelligent transportation systems. In response, many researchers are tackling the issue with technical approaches. In particular, data perturbation schemes, where all sensor data are processed on the user-side, are prospective techniques for utilizing personal data collected by embedded systems and sensor-equipped devices. However, existing data perturbation schemes require sharing the protocol between the user-side and the server-side, even though this can allow malicious attackers to estimate the original sensor data from the perturbed data. In this context, the paper presents a protocol-hidden perturbation framework, called Democratic Privacy, with which a service provider can acquire the original data probability without sharing the perturbation protocol with each user. In Democratic Privacy, the perturbation protocol is selected dramatically by each user, and the original data are reconstructed based on the selection of the crowd. Consequently, malicious attackers cannot distinguish the individual settings by eavesdropping. In addition, this paper presents Three-level Perturbation as one of the methods for Democratic Privacy, and evaluates it by means of simulations. The results of our evaluation demonstrate that Democratic Privacy provides a secure platform for pervasive computing environments.

Community sensing is an emerging paradigm that enables the increasing number of mobile device users to share the minute statistics collected by themselves. In particular, this system is expected to be used for medical and public health research studies, as these mobile devices are in close proximity of the users almost at all times. However, since the mobile devices collect users' sensitive information, a number of privacy concerns will hinder the spread of community sensing applications for medical research. Therefore, we require an environment that enables general users to join community mobile sensing. A widely known technique for preserving privacy in mobile sensing is data perturbation, which adds noises on the user side and allows the central server to reconstruct the statistics of the original data. In this paper, we review a critical vulnerability of state-of-the-art perturbation schemes in which a malicious attacker may restore users' sensitive information from the perturbed data through long-term monitoring attacks. To overcome such vulnerability, we propose privacy-preserving community sensing with multidimensional randomized response, in which all sensed data are processed twice. Using our scheme, we are able to collect users' medical information with security. We evaluate how our scheme can preserve privacy while maintaining the data integrity of aggregated information.

Location estimation with artificial infrastructures for mobile computing has been actively studied, but originally, people get a lot of information from nature during a course of a day. Nature including animals and plants supplies various information acoustically and visually. In this context, we present the concept of Cicada Fingerprinting System, which is a future localization scheme that will enable us to make the most of the information from nature. In our system, users collect the chirp of cicadas as acoustic data via smartphone embedded with microphone. Using the chirp of cicadas such as Wi-Fi fingerprinting, we can specify the location of users regardless of the existence of a roof. That is to say, cicada fingerprinting system applies cicada's instinctive behaviour to a localization. Furthermore, by our system, users are able to feel a sense of belonging to a nature even in urban area, where we spend much time in daily life. This novel system is designed for making general users conscious of presence of nature around.