Automated Vehicles

This study offers a statewide perspective on three areas of the sharing economy— homesharing, ridesharing and bikesharing—and associated responses by communities to growth in these sectors. It evaluates data on the prevalence of these services throughout the state to assess notable patterns and gaps in coverage. Results show that collaborative economic activity is widespread and rapidly expanding into lower-density and more suburban locations throughout the state. In response, municipalities are becoming savvy, establishing creative regulations and collaborations intended to ensure that the sharing economy is aligned with economic development and planning goals.

In this paper, we demonstrate a driver intent inference system that is based on lane positional information, vehicle parameters, and driver head motion. We present robust computer vision methods for identifying and tracking freeway lanes and driver head motion. These algorithms are then applied and evaluated on real-world data that are collected in a modular intelligent vehicle test bed. Analysis of the data for lane change intent is performed using a sparse Bayesian learning methodology. Finally, the system as a whole is evaluated using a novel metric and real-world data of vehicle parameters, lane position, and driver head motion.

The continuous integration of advanced driver-assist systems (ADAS) into connected and autonomous vehicles (CAV) is accelerating the transition of human-driven vehicles to a fully driverless option. The most available ADASs in current running vehicles with their functions, level of autonomy, and present scenario of CAV deployment are critically reviewed and summarized in this paper. The expected advantages with the probable and observed uncertainties of the CAV deployment are also presented. The technological accomplishments (advanced digital infrastructures along with other technologies) are incorporating rapid improvement in vehicle automation. This paper reviews the engagement of technologies in the functioning of automated vehicles and their implementation challenges. Physical infrastructures facilitate the features of automation and connectivity in moving on the roads. Various roadside infrastructures with the issues in assisting the CAV for path tracking are summarized in this paper. The reduction of lane width due to lane-keeping ADAS and replacement of human drivers (i.e., eye) by machine impacts the various geometric elements of highways. The reduced lane width and truck platooning from the integration of cooperative adaptive cruise control affect the structural performance of the pavements significantly. This paper addresses the effects of connected and automated driving on geometric elements and the structural performance of highways. In addition, various techniques to minimize the additional distresses of connected and automated driving are also explained in this paper.

To plan safe trajectories in urban environments, autonomous vehicles must be able to quickly assess the future intentions of dynamic agents. Pedestrians are particularly challenging to model, as their motion patterns are often uncertain and/or unknown a priori. This paper presents a novel changepoint detection and clustering algorithm that, when coupled with offline unsupervised learning of a Gaussian process mixture model (DPGP), enables quick detection of changes in intent and online learning of motion patterns not seen in prior training data. The resulting long-term movement predictions demonstrate improved accuracy relative to offline learning alone, in terms of both intent and trajectory prediction. By embedding these predictions within a chanceconstrained motion planner, trajectories which are probabilistically safe to pedestrian motions can be identified in real-time. Hardware experiments demonstrate that this approach can accurately predict pedestrian motion patterns from onboard sensor/perception data and facilitate robust navigation within a dynamic environment.

Unmanned Aerial Vehicles (UAVs) have been extensively studied to complete the missions in recent years. The UAV trajectory planning is an important area. Different from the commonly used methods based on path search, which are difficult to consider the UAV state and dynamics constraints, so that the planned trajectory cannot be tracked completely. The UAV trajectory planning problem is considered as an optimization problem for research, considering the dynamics constraints of the UAV and the terrain obstacle constraints during flight. An hp-adaptive Radau pseudospectral method based UAV trajectory planning scheme is proposed by taking the UAV dynamics into account. Numerical experiments are carried out to show the effectiveness and superior of the proposed method. Simulation results show that the proposed method outperform the well-known RRT* and A* algorithm in terms of tracking error.

In the field of cyber-security, software performance optimization is a major focus of research to better prevent cyber threats. However, once threats are detected, they have to be managed by a human operator or more often by human operators' joint actions. The purpose of this study is to show that in these collaborative situations, the interpersonal trust level between these actors shapes their handling of the threat. Forty-five participants performed, with twenty-eight different fictive teammates, a collaborative counting task that included aleatory phases of jamming. Each fictive teammate was described through two adjectives selected to induce a predefined level of interpersonal trust (low or high). The subject and his collaborator worked on different systems with different objects to count and different jamming phases. Nevertheless, each participant had the possibility of supervising his teammate's work by checking out his task and modifying his answers (number of targets...

A switching adaptive control algorithm for automating connected vehicles in a rigid platoon pattern is proposed here. A second-order nonlinear model for the follower vehicles running on the highways is adopted and it is assumed that the parameters of the vehicles's model, including the mass, aerodynamic drag and tire drag, are fully unknown and their values cannot be used in arriving at the control laws. Furthermore, some uncertainties and external perturbations are added to the model to consider the effects of always present modeling errors, un-modeled dynamics and external time varying perturbations on the vehicles. Besides, control input variations are inserted into the nonlinear model of the platoon to represent actuator fluctuations. Subsequently, a robust adaptive control scheme is established so that the asymptotic stability of each vehicle in the platoon is guaranteed, and this is demonstrated using the Lyapunov stability criterion. A novel spacing error variable is also introduced to achieve the global string stability for the whole platoon. Following a comprehensive mathematical analysis, a computer simulation example is presented to illustrate the effectiveness as well as the performance of the proposed control system.

Communication between automated vehicles (AVs) and vulnerable road users (VRUs) is highly relevant in coordinating traffic maneuvers and therefore ensuring road safety. Especially in shared spaces such as parking areas, communication is highly important. As automated driving changes the driver's role, communication between different traffic participants will also change. External human-machine interfaces (eHMIs) may enhance safety and effective communication between VRUs and AVs by providing relevant information to other traffic participants if informal communication is insufficient. Hence, a variety of information (e.g., about AVs' driving mode or future maneuvers) is recommended to be communicated to VRUs. Therefore, we investigated the effects of three different light signals, presented by a light bar placed on the test vehicle's roof, as a form of an eHMI: automation mode, starting mode and crossing mode. Moreover, two different driving conditions (i.e., a manual and a simulated automated driving condition) were implemented to investigate the effects between these conditions. Either the driver was visible in the manual condition or the vehicle appeared driverless as a seat suit covered the driver in the simulated automated condition (Wizard-of-Oz design). A total of N = 173 random pedestrians passing by were interviewed and behavioral data were collected from over 1500 pedestrians. Results indicate that participants felt significantly safer during the interaction with the vehicle when a driver was visible. Although VRUs evaluated the general approach of applying light signals as eHMIs for AVs as useful, they assessed the presented light signals as only partially trustworthy and rather unintuitive. Moreover, many participants were unsure of whether the light signals were directed towards them, thus the directedness of light signals should be considered when implementing a light-based eHMI in AVs. Further, moving light signals attracted more attention (i.e., increased pedestrians' head movements towards the vehicle) than a steady or no light signal did. Interestingly, no difference existed between the investigated steady light signal and the baseline condition (i.e., no light signal) regarding head movements towards the vehicle. The results underline the importance of implementing an appropriate eHMI design in AVs.

As technical realization of highly and fully automated vehicles draws closer, attention is being shifted from sheer feasibility to the question of how an acceptable driving style and thus comfort can be implemented. It is increasingly important to determine, how highly automated vehicles should drive to ensure driving comfort for the now passive drivers. Thus far, only little research has been conducted to examine this issue. In order to lay a basis on how automated vehicles should drive to ensure passenger comfort, different variations of three central maneuvers were rated and analyzed. A simulator study (N = 72) was conducted in order to identify comfortable driving strategies. Three variations of lane changes, accelerations and decelerations were configured by manipulating acceleration and jerk, and thus the course of each maneuver. Furthermore, the influence of personality traits and self-reported driving style on preferences of differently executed automated maneuvers was analyzed. Results suggest keeping acceleration and jerk as small as possible for acceleration maneuvers. For lane changes, both small accelerations as well as an early motion feedback are advisable. Interestingly, decelerating as a manual driver would is rejected compared to two artificial alternatives. Moreover, no influence of personality traits on maneuver preference was found. Only self-reported driving style had a marginal effect on participants' preferences. In conclusion, a recommendation for an automated driving style can be given, which was perceived as comfortable by participants regardless of their personality.

Rapid urbanization in Mashhad, Iran's second-largest city and a major religious hub, has intensified transportation challenges, including congestion, air pollution, and inadequate last-mile connectivity. This study proposes a data-driven urban development framework leveraging intelligent transportation systems (ITS) to enhance travel efficiency and sustainability. The framework integrates advanced geodatabases, real-time data exchange interfaces, and open platforms for urban mobility analytics. Using a mixed-methods approach, including MICMAC structural analysis and stakeholder surveys, we identify key drivers and barriers to ITS implementation. Virtual modeling and simulated traffic data demonstrate the framework's feasibility, showing a potential 15% reduction in peak-hour congestion. This research underscores the importance of integrated data governance, stakeholder collaboration, and smart mobility innovations to support sustainable urban growth in Mashhad.

Optimization of passenger matching to improve service efficiency and customer happiness is both possible and difficult in the ride-hailing sector, which has grown rapidly. This paper proposes optimizing ride-hailing passenger matching using IoT and reinforcement learning (RL). To gather real-time traffic, passengers, and vehicle data, integrate Internet of Things (IoT) sensors into ride-hailing vehicles. Using IoT data, an RL agent dynamically assigns passengers to drivers. Journey duration, driver proximity, and passenger preferences are considered by the RL model to match passengers. This technique improves passenger matching efficiency and reduces wait times and journey durations via thorough simulations and real-world trials. Our findings show that the IoT-integrated RL system outperforms heuristicsbased systems, making ride-hailing more smooth and rewarding for passengers and drivers. It explores the scalability and practical application of our approach on current ride-hailing platforms, emphasizing the potential for considerable service quality and operational cost savings. It addresses IoT data privacy and security problems and recommends solutions to protect user data.

This paper presents a demonstration of the implementation and performance results of the SCATS V2I system for managing FIFA bus transit during the 2022 World Cup in Doha, Qatar to assist hosting more than 1.4 million people from across the globe in a smart way. The goal of the project was to streamline bus operations for seamless and efficient transit between key event venues, improve traffic flow, and reduce congestion. SCATS (Sydney Coordinated Adaptive Traffic System), as an intelligent traffic management system, during this task integrated with the buses' V2I communication systems, to enable real-time optimization of signal timings based on bus priorities and traffic condition. This paper provides insights into system architecture and performance metrics of the SCATS V2I system, demonstrating its benefits for public transportation during a high-profile event. Through the integration of the Qatar Bus Fleet Management System with SCATS SPE, the Qatar Road Management Centre was able to synchronized 150 traffic signals for 327 FIFA buses by 11,395 times.

The current paradigm for advancing autonomous driving functionalities on public roads predominantly operates within the tangible realm. Autonomous vehicles (AVs) are equipped with an array of sensors, including Lidar, cameras, radar, GPS, and on-boardunit (OBU) modems, each actively collecting data about the surrounding environment. This data is then processed through sophisticated algorithms, enhancing the vehicle's situational awareness. In our pursuit of refining autonomous driving capabilities, we leverage the AVTSC Simulator as our chosen Autonomous Vehicle (AV) simulation environment. Notably, this simulator stands out for its ability to replicate realistic urban settings and seamlessly integrate with widely used platforms such as Apollo and ROS. By harnessing the capabilities of this simulation environment, we aim to bridge the gap between real-world testing and simulated scenarios. This abstract encapsulates the essence of our strategy, emphasizing the integration of advanced sensor technologies and simulation environments for the development of autonomous driving features. Preliminary results indicate a promising 94% success rate for the proposed methodology, showcasing the efficacy of our approach in enhancing the robustness and reliability of autonomous systems.

This paper describes the recent research on the enhancement of pedestrian safety to help develop a better understanding of the nature, issues, approaches, and challenges surrounding the problem. It presents a comprehensive review of research efforts underway dealing with pedestrian safety and collision avoidance. The importance of pedestrian protection is emphasized in a global context, discussing the research programs and efforts in various countries. Pedestrian safety measures, including infrastructure enhancements and passive safety features in vehicles, are described, followed by a systematic description of active safety systems based on pedestrian detection using sensors in vehicle and infrastructure. The pedestrian detection approaches are classified according to various criteria such as the type and configuration of sensors, as well as the video cues and classifiers used in detection algorithms. It is noted that collision avoidance not only requires detection of pedestrians but also requires collision prediction using pedestrian dynamics and behavior analysis. Hence, this paper includes research dealing with probabilistic modeling of pedestrian behavior for predicting collisions between pedestrians and vehicles.

The potential of an efficient ride-sharing scheme to significantly reduce traffic congestion, lower emission level and drivers' stress, as well as facilitating the introduction of smart cities has been widely demonstrated in recent years . Furthermore, ride sharing can be implemented within a sound economic regime through the involvement of commercial services that creates a win-win for all parties (e.g., Uber, Lyft or Sidecar [2][3]). This positive thrust however is faced with several delaying factors, one of which is the volatility and unpredictability of the potential benefit (or utilization) of ride-sharing at different times, and in different places. Better understanding of ride-sharing dynamics can help policy makers and urban planners in increase the city's "ride sharing friendliness" either by designing new ride-sharing oriented systems, as well as by providing ride-sharing service operators better tools to optimize their services. In this work the following research questions are posed: (a) Is ride-sharing utilization stable over time or does it undergo significant changes? (b) If ride-sharing utilization is dynamic, can it be correlated with some traceable features of the traffic? and (c) If ride-sharing utilization is dynamic, can it be predicted ahead of time? We analyze a dataset of over 14 Million taxi trips taken in New York City. We propose a dynamic travel network approach for modeling and forecasting the potential ride-sharing utilization over time, showing it to be highly volatile. In order to model the utilization's dynamics we propose a network-centric approach, projecting the aggregated traffic taken from continuous time periods into a feature space comprised of topological features of the network implied by this traffic. This feature space is then used to model the dynamics of ride-sharing utilization over time. The results of our analysis demonstrate the significant volatility of ride-sharing utilization over time, indicating that any policy, design or plan that would disregard this aspect and chose a static paradigm would undoubtably be either highly inefficient or provide insufficient resources. We show that using our suggested approach it is possible to model the potential utilization of ride sharing based on the topological properties of the rides network. We also show that using this method the potential utilization can be forecasting a few hours ahead of time. One anecdotal derivation of the latter is that perfectly guessing the destination of a New York taxi rider becomes nearly three times easier than rolling a "Snake Eyes" at a casino.

Motion sickness is a persistent problem in many forms of transport. It affects most of the population, is debilitating for the sufferer and can disrupt the journey for the rest. Automated Vehicles (AV‟s) offer greater flexibility in cabin design particularly in the future where no physical controls are required. This poses additional risks to passenger wellbeing with increased levels of motion sickness when passengers and historical drivers are multi-tasking. This study demonstrates a device that can predict real time occupant motion sickness based on motion, head tilt and ambient conditions. Recovery is also considered for multiple journeys. The device can be easily modified to reflect an individual‟s susceptibility or use group settings for the general population. Index Terms Automated Vehicle, Motion sickness, Wellbeing, Prediction.

In this paper, a cooperative decision-making is presented, which is suitable for intention-aware automated vehicle functions. With an increasing number of highly automated and autonomous vehicles on public roads, trust is a very important issue regarding their acceptance in our society. The most challenging scenarios arise at low driving speeds of these highly automated and autonomous vehicles, where interactions with vulnerable road users likely occur. Such interactions must be addressed by the automation of the vehicle. The novelties of this paper are the adaptation of a general cooperative and shared control framework to this novel use case and the application of an explicit prediction model of the pedestrian. An extensive comparison with state-of-the-art algorithms is provided in a simplified test environment. The proposed approach is also implemented on the experimental vehicle. The results show the superiority of the proposed model-based algorithm and its suitability for real-world applications.

Social and economic trends have strongly changed in the last years due to the economic crisis and the evolution of technology. These factors have influenced a sharing revolution, also in the mobility sector motivated for the increasing urbanisation and environmental consciousness. The paper focuses on the intended use of shared mobility services by citizens of the metropolitan Barcelona region, relying on a quantitative analysis of their mobility patterns, behaviours, needs and expectations. Six hundred surveys with commuting travellers were conducted in order to identify the differences among customers regarding different factors, such as their age, daily trips or personal incomes. Results show clear different patterns depending on whether commuting trips are within or out of the city and a greater intended use of ridesharing, carsharing and ride-hailing services of the youngest population. Besides, data indicates that travellers do not have preferences for a single mean of transport but for the service that best meets their needs in each occasion.

Shared ride-hailing transportation is discreetly emerging in cities all over the world with the purpose of decongesting cities, offering a similar comfort and convenience of the private car, and this way, filling the gap between the cities' bus services and the regular taxi services. Hence, the aim of this research is to detect the main factors that should be taken into account for the service design of shared ridehailing transportation, as well as to compare this mean of transport with the bus and taxi services. The research was conducted through, first, an analysis of shared ride-hailing services business model. Then, a quantitative research was conducted to users of a small-scale one-week pilot in Barcelona. The results show that factors related to price and travel times are more important from a user perspective than the ones related to comfort, such as the walking distance to the pickup point or the comfort provided by the vehicle. Furthermore, results indicate the intended use of participants of a potential future shared ride-hailing service in different use cases.

aimed to investigate factors that influence comfort in automated driving as well as to identify physiological indicators of discomfort. In an empirical driving simulator study, 40 participants from 25 to 84 years old experienced three highly automated trips including six potentially uncomfortable situations in each trip. Participants reported perceived discomfort continuously by a handset control. The physiological parameters Heart Rate (HR) and Electrodermal Activity (EDA) were assessed using the smartband Microsoft Band 2; pupil diameter and eye blinks were measured by the SMI Eye Tracking Glasses 2. Results showed specific physiological reactions in situations that provoked moderate to high discomfort. Longer lasting and slowly evolving situations with lower reported discomfort did not show associated changes in physiological parameters. HR decreased consistently during uncomfortable situations, which could be related to the phenomenon ''preparation for action". Pupil diameter increased and eye blink rate decreased in uncomfortable situations that were visually monitored. EDA did not show specific effects, which, however could be attributed to measurement procedures of the smartband. The results serve as a basis for developing a real-time discomfort detection algorithm and will additionally be validated on-road.

Recommendation: Mn/DOT should lead and support efforts to increase the infusio n and benefits of technology in CBT operations. Opportunities for information exchange and training should be provided along with technology. Incentives for small providers to expand their service, including pro-active standardization of regulations and equipment, should be promulgated. Carsharing also presents significant opportunities for the immigrant population. In addition, it could bring the benefits of private automobile ownership to those who typically cannot afford a car of their own, or where ownership is otherwise inefficient, such as university campuses. However, service to these populations is "uncharted territory" for car-sharing organizations, and, in some cases, the service will require continued operating subsidies. Nonetheless, the potential benefits should be examined, and, where possible, quantified. Recommendation: Mn/DOT should support efforts to make carsharing a reality where it will bring greatest benefits, but also where the proper partners exist. The Technical Advisory Panel for this project has identified two types of partners for whom the time may be right: real estate developers and innovative insurance companies. More specific suggestions for these are included in Appendix W. The State of Minnesota should support efforts to bring carsharing to non-traditional car-sharing markets, such as close-knit ethnic groups and university populations. Telecommunications and ATIS each also have areas where significant benefits can be achieved. These areas, however, require further research before they can be effectively deployed. As discussed in this study, those who are about to retire, and the baby-boom population, which is likely to begin retiring in the next decade, are likely to benefit from advances in these areas. Recommendations : Mn/DOT should support continued research into evaluation and definition of high-quality ATIS services and should continue to monitor efforts by telecommunications providers to expand e-services, especially in greater Minnesota. ATIS also provides opportunities to enhance the other applications. Currently, ATIS exists in the form of "511" services, which provide information about travel delay and other items useful to automobile travelers over the telephone. Further development of ATIS could allow it to also include information about alternative modes, such as CBT. This service also could link to reservation systems of CBT providers or even car-sharing organizations. Certainly, the participation of CBT providers and car-sharing organizations would be needed. However, other potential partners could include employers, who could provide a base for organizing CBT operations, and/or large employers that have automobile fleets, including state and local governments, as potential partners for car-sharing organizations to share vehicles during off-peak times. Further research into the interest of these organizations in working together, the ability to add information from these providers in a centralized fashion, and the ease of customizing ATIS services for user needs will be needed to make this kind of "one-stop" service a reality.

The market for personal mobility is changing rapidly due to shifting social and cultural trends, as well as technological advances, such as smartphones, information processing, widespread data connectivity, sharing, and vehicle automation. Shared, on-demand mobility represents a sustainable vision for future mobility with a reliable network of multimodal options that are available to all travelers. On March 22, 2019, the Local Government Commission (LGC) and the Transportation Sustainability Research Center (TSRC) at the University of California, Berkeley hosted the Caltrans Shared Mobility and Policy Toolkit Workshop. The workshop facilitated a dialogue of approximately 100 participants representing local, state, and regional governments; private companies; nonprofits and community-based organizations; and educational institutions.

Shared mobility-the shared use of a vehicle, bicycle, or other mode-is an innovative transportation strategy that enables users to gain short-term access to transportation modes on an "as-needed" basis. The term shared mobility includes various forms of carsharing, bikesharing, ridesharing (carpooling and vanpooling), and on-demand ride services. It can also include alternative transit services, such as paratransit, shuttles, and private transit services, called microtransit, which can supplement fixed-route bus and rail services. With many new options for mobility emerging, so have the smartphone "apps" that aggregate these options and optimize routes for travelers. In addition to innovative travel modes, new ways of transporting and delivering goods have emerged. These "courier network services" have the potential to change the nature of the package and food delivery industry. Shared mobility has had a transformative impact on many global cities by enhancing transportation accessibility, while simultaneously reducing driving and personal vehicle ownership. A number of environmental, social, and transportation-related benefits have been reported due to the use of various shared mobility modes. Several studies have documented the reduction of vehicle usage, ownership, and vehicle miles or kilometers traveled (VMT/VKT). More research is needed, nevertheless, to further understand impacts on a city and regional level and across the wide range of shared mobility modes. Shared mobility could also extend the catchment area of public transit, potentially playing a pivotal role in bridging gaps in existing transportation networks and encouraging multi-modality by addressing the first-and-last mile issue related to public transit access. Furthermore, shared mobility could also provide economic benefits in the form of household cost savings, increased economic activity near public transit stations and multi-modal hubs, and increased access. This white paper includes an introduction and background to different types of shared modes, as well as smartphone-based trip planning apps that can facilitate access to public transit and shared mobility services. This paper also notes where potential benefits of shared mobility could align with the new mission of the California Department of Transportation (Caltrans), which is to "Provide a safe, sustainable, integrated, and efficient transportation system to enhance California's economy and livability" (Caltrans, 2015a). We conclude the paper with a summary and provide an appendix with a glossary of terms and a list of the shared mobility models, including a range of companies in each sector.

In recent years there has been significant worldwide activity in shared-use vehicle systems (i.e., carsharing and station cars). Much of this activity is taking place in Europe and North America; however, there has also been significant activity in Asia, primarily in Japan and Singapore. This paper examines the latest shared-use vehicle system activities in both of these countries, beginning with an historical review followed by an evaluation of their current systems. Overall there are several well-established systems in both Japan (approximately 18 systems, 150 vehicles, 3000 members) and Singapore (approximately 4 systems, 300 vehicles, and 8000 members). In contrast to most European and North American cities, both Japan and Singapore already have a wide range of viable public transportation modes. Interestingly, the primary carsharing focus in Japan is on business use and on neighborhood residential in Singapore. This is likely due to limited vehicle licensing and high car ownership costs in Singapore. Further, systems in Japan and Singapore have a high degree of advanced technology in their systems, making the systems both easy to use and manage. The member-vehicle ratios in Asia appear to be approximately the same as Europe and Canada and less than the U.S. It is expected that Asian shared-use vehicle systems will continue to have steady growth in terms of number of organizations, vehicles, and users.

Autonomous shuttles characterize a new, rising segment of
transportation research. While previous studies and deployments
of self-driving technology was initially more centered on
autonomous cars, autonomous shuttles for shared transit began to
spark up interest as they present an opportunity to create new
advancement in mobility. Therefore, the objective of this research
is to study the deployment of fully autonomous shuttles using the
industrial area of Carros-Le Broc in the South-East of France as a
case study. This report first describes the conception of a service
based on an autonomous shuttle and highlights the potential
benefits for a territory like Carros-Le Broc. In order to make
autonomous shuttles a reality, our work then led us to propose an
index to accompany the deployment of this new modality .  The
research unveiled the needs for the successful integration of an
autonomous shuttle into a territory, and seven main themes were
identified : (1) connectivity overview; (2) mapping; (3)
infrastructure readiness ; (4) vehicle-to-everything communication;
(5) accessibility factors ; (6) user acceptability; and (7) Regulatory
framework. The study of Carros-Le Broc area is thus completed
with an application of the index, revealing the current status of
the area and where it should transition to be ready to integrate for
autonomous shuttle. Altogether, this project intends to support the
development and adoption of the autonomous shuttles as a new
mode of transportation.

In transportation and aerospace, more automation and autonomy are continuously added to systems. The ability of human operators to effectively monitor and interact with these systems, poses significant challenges. This research focuses on critical decisions that largely rely on the system capabilities but need to be validated and made under the responsibility of the operator. In the context of unmanned combat air vehicles (UCAV), the experiment focuses on how the communication strategy of semi-autonomous systems modifies the operators' understanding of the situation and the final decision. The study has been conducted in an immersive simulator with a 30 minutes ecological military scenario where the operator had to manage a full mission, including an unplanned missile firing decision. The experiment included the use of physiological measures related to electrodermal and cardiac activities. The paper reports the results of the decision-making performances and the analyses of the physiological parameters. It appears that the communication strategy has an impact on the situation awareness of the operator, the decision taken, and the evolution of the physiological parameters.

In this paper, we consider the problem of platooning control with mismatched disturbances using the distributed adaptive backstepping method. The main challenges are: (1) maintaining the compositionality and the distributed nature of the controller, and (2) ensuring the robustness of the controller with respect to general types of disturbances. To address these challenges, we first propose a novel notion that we named Vector String Lyapunov Function, whose existence implies l2 weak string stability. This notion is based on the vector Lyapunov function-based stability analysis, which depends on the inputto-state-stability of a comparison system. Using this notion, we propose an adaptive backstepping controller for the platoon such that the compositionality and the distributed nature of the controller can be ensured while the internal stability and string stability of the closed-loop system are formally guaranteed. Finally, simulation examples are provided to illustrate the effectiveness of the proposed control algorithm. In particular, we provide simulation results comparing our proposed control algorithm with a recently proposed control algorithm from the literature, under two types of information flow topologies and disturbances.

Podcars or Personal Rapid Transit (PRT) has been discussed and analyzed in Sweden since the mid-1960's. Interests in PRT has gone up and down over time. Since the mid-1990's, however, the efforts of promoting PRT in Sweden have become more serious and frequent. Many pre-feasibility and a few feasibility studies were undertaken by researchers, consultants and cities against a wide range of vested interests in the automotive and public transport industries. This paper summarizes a great number of studies, and tries to explain why PRT has not yet materialized. Up to now, the crucial point seems to have been the imbalance between risks and financial support: No local authority can buy a system, due to the development risks as long as it is an unproven technology; and no developer can invest fully in development, as long as there is no (mass) market for the end product.

The existence of reliable public transportation services will be able to reduce public dependence on the use of private vehicles. This goal will be achieved if public transport service quality improves. The Trans Metro Dewata bus is here in Bali with the Buy The Service program. This program is to buy urban mass transportation services to operators based on the services carried out according to the kilometres travelled. The operator operates 105 buses to serve 4 corridors. Technical and operational performance shows that the indicators of travel time, headway time, waiting time, stopping time, and travel speed follow the minimum service standards and technical guidelines for urban public transport. In contrast, the circulation time in corridors 1 and 4 exceeds the standard. Likewise, the load factor level of 2.40 – 32.65% is not following the performance standards of public transportation, which should be at least 70%. This is due to route accessibility factors, route effectiveness,...

Automated vehicles (AVs) have the potential to cause profound shifts across a wide range of areas of human life, including economic structures, land use, lifestyles and personal well-being. Most current social science on AVs is narrowly framed. Research on public attitudes has focused on whether people are likely to accept and use AVs. We contend that failing to anticipate a wider range of profound social implications may have serious negative consequences, and that social scientists from a range of disciplinary perspectives can provide invaluable insights. Our conclusions are the product of a workshop in London held in 2018 to discuss the place of social science research in relation to the development of AVs. This paper summarises a core selection of our concerns, interests, theoretical and substantive points of reference and aspirations for a constructive role in this field of research and development.

Vehicle control by humans is possible because the central nervous system is capable of using visual information to produce complex sensorimotor actions. Drivers must monitor errors and initiate steering corrections of appropriate magnitude and timing to maintain a safe lane position. The perceptual mechanisms determining how a driver processes visual information and initiates steering corrections remain unclear. Previous research suggests two potential alternative mechanisms for responding to errors: (i) perceptual evidence (error) satisficing fixed constant thresholds (Threshold), or (ii) the integration of perceptual evidence over time (Accumulator). To distinguish between these mechanisms an experiment was conducted using a computer-generated steering correction paradigm. Drivers (N=20) steered towards an intermittently appearing 'road-line' that varied in position and orientation with respect to the driver's position and trajectory. One key prediction from a Threshold framework is a fixed absolute error response across conditions regardless of the rate of error development, whereas the Accumulator framework predicts that drivers would respond to larger absolute errors when the error signal develops at a faster rate. Results were consistent with an Accumulator framework, thus we propose that models of steering should integrate perceived control error over time in order to accurately capture human perceptual performance.

External Human-Machine Interfaces (eHMIs) are expected to bridge the communication gap between an automated vehicle (AV) and pedestrians to replace the missing driver-pedestrian interaction. However, the relative impact of movement-based implicit communication and explicit communication with the aid of eHMIs on pedestrians has not been studied and empirically evaluated. In this study, we pit messages from an eHMI against different driving behaviors of an AV that yields to a pedestrian to understand whether pedestrians tend to pay more attention to the motion dynamics of the car or the eHMI in making road-crossing decisions. Our contributions are twofold: we investigate (1) whether the presence of eHMIs has any objective effect on pedestrians’ understanding of the vehicle’s intent, and (2) how the movement dynamics of the vehicle affect the perception of the vehicle intent and interact with the impact of an eHMI. Results show that (1) eHMIs help in convincing pedestrians of the vehic...

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Planning for autonomous vehicles (AVs) depends on understanding how commuters behave and respond to this disruptive technology. There is a growing body of research on individuals' preferences and perceptions towards AVs, but much less work has been done on the implications for smaller communities. This paper examines commuters' preferences for autonomous vehicles in small and mid-sized US metropolitan areas. Using a nationalwide stated experiment dataset, we find that AVs, both shared and privately owned, would have limited appeal to most of the commuters in smaller urban and rural areas, even when monetary cost of using AVs can be substantially reduced. Also, most of the commuters are not comfortable with sharing the road space with AVs. Our findings point to the importance of designing AV policies that recognize the heterogenous response of users and non-users in small urban and rural areas.

Last mile delivery has become increasingly difficult in urban areas; as population increases alongside the demand of the citizens, novel and smarter solutions are needed to cope with challenges of congestion and misuse of urban spaces, while at the same time sustainability goals need to be taken into consideration. The smart city concept has become the next step for the future transformation of our society. Smart cities strive towards creating better welfare for its citizens in an urban area to reach sustainable consumption of resources. This is done by leveraging technological tools such as IoT, Big Data analytics and ICT. Similarly, smart cities include smart parking which helps to facilitate the process of monitoring, controlling, and parking by the mentioned digital and technological tools. However, previous and current smart parking solutions have almost entirely been developed for passenger cars and not for commercial freight vehicles which park on so called loading zones (LZs), in order to perform loading/unloading activity for parcels and goods delivery.

In this paper, we consider the problem of vehicle collection assisted by a fleet manager where parked vehicles are collected and guided by fleet managers. Each platoon follows a calculated and optimized route to collect and guide the parked vehicles to their final destinations. The Platoon Route Optimization for Picking up Automated Vehicles problem, called PROPAV, consists in minimizing the collection duration, the number of platoons and the total energy required by the platoon leaders. We propose a formal definition of PROPAV as an integer linear programming problem, and then we show how to use the Non-dominated Sorting Genetic Algorithm II (NSGA-II), to deal with this multi-criteria optimization problem. Results in various configurations are presented to demonstrate the capabilities of NSGA-II to provide well-distributed Paretofront solutions.

In this paper, we consider the problem of vehicle collection assisted by a fleet manager where parked vehicles are collected and guided by fleet managers. Each platoon follows a calculated and optimized route to collect and guide the parked vehicles to their final destinations. The Platoon Route Optimization for Picking up Automated Vehicles problem, called PROPAV, consists in minimizing the collection duration, the number of platoons and the total energy required by the platoon leaders. We propose a formal definition of PROPAV as an integer linear programming problem, and then we show how to use the Non-dominated Sorting Genetic Algorithm II (NSGA-II), to deal with this multi-criteria optimization problem. Results in various configurations are presented to demonstrate the capabilities of NSGA-II to provide well-distributed Paretofront solutions.

Advanced Driver Assistance Systems (ADAS) play a pivotal role in modern road vehicles, enhancing safety. However, persistent challenges in managing ADAS systems and automated vehicles during dynamic traffic scenarios hinder the widespread adoption of ADAS and Automated Driving (AD). Recognizing the susceptibility of perception sensors to weather and road hazards, along with their typical operational limitations, V2X communication becomes critically important to achieve higher levels of autonomy and robustness. In the EU-funded project ESRIUM, safety improvement is attained by developing a digital map capable of accurately identifying road damage and offering real-time recommendations for connected vehicles. In this paper, we report the implementation and road testing results for the infrastructure-assisted AD system developed within the ESRIUM project. These tests were conducted on the public Austrian highway A2 under typical driving conditions, showcasing the effectiveness of infrastructureassisted AD vehicles in diverse traffic scenarios. The findings represent a significant advancement in validating automated vehicles on operational highways, emphasizing the vital role of infrastructure and V2X communication in enhancing ADAS/AD road safety and efficiency.

Automated vehicles (AVs) have the potential to cause profound shifts across a wide range of areas of human life, including economic structures, land use, lifestyles and personal well-being. Most current social science on AVs is narrowly framed. Research on public attitudes has focused on whether people are likely to accept and use AVs. We contend that failing to anticipate a wider range of profound social implications may have serious negative consequences, and that social scientists from a range of disciplinary perspectives can provide invaluable insights. Our conclusions are the product of a workshop in London held in 2018 to discuss the place of social science research in relation to the development of AVs. This paper summarises a core selection of our concerns, interests, theoretical and substantive points of reference and aspirations for a constructive role in this field of research and development.

The Solar Skyways project envisions a transformative approach to urban transportation by developing a solar-powered, fully automated, elevated personal rapid transit network. This innovative system aims to alleviate congestion, reduce carbon emissions, and enhance the efficiency of urban mobility. By leveraging renewable energy and cutting-edge automation technology, Solar Skyways offers a sustainable and futuristic solution to contemporary urban transport challenges. This research explores the technical, environmental, and economic aspects of implementing Solar Skyways, with a focus on its feasibility, benefits, and potential impact on urban environments. Also in the autumn of 2007, a group of 200 people convened in Uppsala, Sweden, to view a prototype and explore collaboration on a new transportation alternative: the podcar. This small cabin operates on a fixed guideway directly above urban streets. Originally described as Personal Rapid Transit (PRT) and more recently as an Automated Transportation Network (ATN), this driverless, on-demand mode of transportation offers much faster and more convenient mobility than a bus, and it is far more efficient and safer than an automobile. Moreover, it is uniquely suited to being powered by solar energy (photovoltaics) directly within the system’s pathway. At that conference, and in subsequent discussions, proponents demonstrated the feasibility of powering this urban transportation system entirely with solar energy, achieving a “net zero” energy demand by selling to and buying from the existing electricity grid. Since 2011, the solar design has been refined through the Solar Skyways Challenge, an ongoing multidisciplinary initiative embraced by students from three universities: in the Netherlands, Sweden, and Silicon Valley, USA.Now in its third year, the Challenge continues with students at San Jose State University and other institutions forming multidisciplinary teams to design and build computer-controlled scale models and full-scale system components. Their ultimate goal is to create a full-scale test track that demonstrates the technical, environmental, and economic viability of Solar Skyways.
Keywords: Transportation; Podcars; Solar Skyways; Solarevolution; Capstone Project; Uppsala; SMSSV

Understanding the effects of having automated vehicles in the future traffic scenarios is an important research topic that attracts a great deal of attention currently. The difficulty in studying this problem is the fact that real life measurement and testing of these scenarios can not be made as there are still a very small fraction of automated vehicles in the traffic. So analyzing and understanding the effects of mixed traffic requires extensive simulative analysis. In this paper we analyze this problem using real traffic data in combination with the open-source SUMO traffic simulation software. The traffic flow is modeled based on the measurement data from a section of the Austrian A2 motorway, while the effects of automated vehicles at various penetration rates is simulated and consequently some observation are made.

Autonomous vehicles (AVs) have the potential to improve road safety. Trust in AVs, especially among pedestrians, is vital to alleviate public skepticism. Yet much of the research has focused on trust between the AV and its driver/passengers. To address this shortcoming, we examined the interactions between AVs and pedestrians using uncertainty reduction theory (URT). We empirically verified this model with a user study in an immersive virtual reality environment (IVE). The study manipulated two factors: AV driving behavior (defensive, normal and aggressive) and the traffic situation (signalized and unsignalized). Results suggest that the impact of aggressive driving on trust in AVs depends on the type of crosswalk. At signalized crosswalks the AV's driving behavior had little impact on trust, but at unsignalized crosswalks the AV's driving behavior was a major determinant of trust. Our findings shed new insights on trust between AVs and pedestrians.

For automated vehicles (AVs) to reliably navigate through crosswalks, they need to understand pedestrians' crossing behaviors. Simple and reliable pedestrian behavior models aid in real-time AV control by allowing the AVs to predict future pedestrian behaviors. In this paper, we present a Behavioraware Model Predictive Controller (B-MPC) for AVs that incorporates long-term predictions of pedestrian crossing behavior using a previously developed pedestrian crossing model. The model incorporates pedestrians' gap acceptance behavior and utilizes minimal pedestrian information, namely their position and speed, to predict pedestrians' crossing behaviors. The B-MPC controller is validated through simulations and compared to a rule-based controller. By incorporating predictions of pedestrian behavior, the B-MPC controller is able to efficiently plan for longer horizons and handle a wider range of pedestrian interaction scenarios than the rule-based controller. Results demonstrate the applicability of the controller for safe and efficient navigation at crossing scenarios. Index Terms-autonomous urban driving, behavior-aware control, autonomous control, social human-robot interaction.