Autonomous Bus Research Articles

Erratum to “Active noise control of vibroacoustic noise from HVAC system in autonomous bus using conjugate gradient-based algorithm”

Erratum to “Active noise control of vibroacoustic noise from HVAC system in autonomous bus using conjugate gradient-based algorithm”

Analysis of Factors Affecting Disengagement Using Actual Driving Data in Level 3 Autonomous Bus

Analysis of Factors Affecting Disengagement Using Actual Driving Data in Level 3 Autonomous Bus

Ethical Decision-Making for the Inside of Autonomous Buses Moral Dilemmas

Ethical Decision-Making for the Inside of Autonomous Buses Moral Dilemmas

Active noise control of vibroacoustic noise from HVAC system in autonomous bus using conjugate gradient-based algorithm

Active noise control of vibroacoustic noise from HVAC system in autonomous bus using conjugate gradient-based algorithm

MLIOM-AB: Multi-LiDAR-Inertial-Odometry and Mapping for Autonomous Buses

MLIOM-AB: Multi-LiDAR-Inertial-Odometry and Mapping for Autonomous Buses

Modelling connected and autonomous bus on dynamics of mixed traffic in partially connected and automated traffic environment

Since connected and autonomous buses (CABs) have great potential of being operated in partially connected environment and raise great impact on mixed traffic flow, this study develops an analytical model for modeling system dynamics of mixing connected and autonomous vehicles (CAV), human-driven vehicles (HV), and CABs in both cases of dedicated bus lane and non-dedicated bus lane, in which the impact of CABs is specifically quantified under varied levels of connected vehicle penetration. A Vissim-Matlab simulation evaluation is also developed for validating the model’s effectiveness. The results demonstrate that the proposed model performs well with an accuracy of over 85%, and it can be up to 95% in case of non-dedicated bus lane when the inflow rate is higher than 2000veh/h. Besides, the impacts of CABs’ volume, bus dwelling time, and bus stop location on the performance of mixed traffic flow are verified in two CAB operating environments. The obtained results can be used as the basis for planning and operating CABs in partially connected environment.

Autonomous Bus Docking for Optimal Ride Comfort of Standing Passengers

Autonomous Bus Docking for Optimal Ride Comfort of Standing Passengers

Trajectory planning and tracking of dynamic lane change for autonomous buses considering vehicle stability in dynamic traffic scenarios

Trajectory planning and tracking of lane change are critical technologies for autonomous buses. Characteristics of the buses susceptible to stability problems resulting from the high height, large passenger capacity and long lengths, coupling the dynamic traffic with the dynamic changes in the states of adjacent vehicles and road adhesion coefficient, put forward great challenges in lane change for autonomous buses (ABs). To cope with the foregoing challenges, a framework is proposed to achieve the trajectory planning and tracking of dynamic lane change for ABs. For trajectory planning approach, the trajectory planning and replanning is optimized in the safe range of longitudinal length of the lane-changing trajectory to obtain the real-time reference trajectory, combining consideration of vehicle yaw, roll stability and lane-changing efficiency. The minimum longitudinal length of lane-changing trajectory determined by the yaw stability and roll stability of ABs, combined with the maximum length formed by the adjacent vehicles with dynamic states, form the real-time safe range for lane-changing trajectory planning. For trajectory tracking approach, a tracking approach using model predictive control based on multipoint preview is proposed to achieve the real-time planned trajectory tracking considering buses stability. The effectiveness of the proposed strategy is evaluated by simulating an experimentally validated Trucksim model in different dynamic traffic scenarios to demonstrate the capability of the strategy in trajectory planning and tracking, and guaranteeing vehicle stability for dynamic lane change of ABs.

If It Concerns Me: An Experimental Investigation of the Influence of Psychological Distance on the Acceptance of Autonomous Shuttle Buses

Autonomous vehicles (AVs) will revolutionize our everyday mobility in the future. However, the prerequisite for this is that the technology is accepted by the population. Currently, AVs are still difficult to grasp for many people, i.e., the topic of autonomous driving is psychologically distant. In other contexts, it has been shown that this psychological distance or proximity can be used to influence product perception. However, the influence of psychological distance has never been investigated in the AV context. To address this research gap, we investigated the impact of psychological distance on the intention to use (ITU) AVs. We manipulated psychological distance in a 2x2x2 scenario-based experiment (N = 2114) on two different dimensions and additionally varied driving modality for comparison purposes: subjects either imagined themselves or an average person (social distance) using either a traditional or autonomous bus (driving modality) either today or in ten years (temporal distance). Our results showed a main effect of driving modality and social distance, with higher ITU for AVs and the average person. Temporal distance interacted with social distance to affect ITU. Interestingly, psychological distance also affected ITU for traditional buses with a similar interaction pattern. Thus, our study suggests that psychological distance affects the ITU of buses in general rather than AV technology. Providers can benefit from framing AVs as temporally close and providing as concrete, detailed information as possible. Future research should examine the underlying mechanisms (e.g., a shift in bus use priorities) that can explain why social distance plays an important role, particularly in future scenarios.

Optimizing Fleet Structure for Autonomous Electric Buses: A Route-Based Analysis in Aachen, Germany

Intelligent transportation systems enhance the potential for sustainable, user-friendly, and efficient transport. By eliminating driver costs, autonomous buses facilitate the redesign of networks, timetables, and fleet structure in a cost-effective manner. The electrification of bus fleets offers the opportunity to further improve the environmental sustainability of transportation networks, but requires adjustments to vehicle schedules due to the limited range and charging requirements. This paper examines the intricate relationship between electrification and autonomous buses. To this end, timetables for autonomous electric buses of different sizes were developed for a real bus route in Aachen, Germany. The resulting electric vehicle scheduling problem was then solved using an adaptive large neighborhood search to determine the number of vehicles needed and the total cost of ownership. By eliminating driver costs, vehicles with lower passenger capacity become much more attractive, albeit at a slightly higher cost. In comparison, the incremental costs of electrification are low if the right approach is taken. Fluctuations in typical passenger numbers can be used to modify timetables and vehicle schedules to accommodate the charging needs of autonomous electric buses. In particular, electric bus concepts with fewer charging stations and lower charging power benefit from adapting the timetable to passenger numbers. The results demonstrate that the specific requirements of electric buses should be considered when adapting networks and timetables in order to design a sustainable transport network.

Less Conservatism, Stronger Robustness: Iterative Robust Gain-Scheduled Path Following Control of Autonomous Bus With Unstructured and Changing Dynamics

Less Conservatism, Stronger Robustness: Iterative Robust Gain-Scheduled Path Following Control of Autonomous Bus With Unstructured and Changing Dynamics

Navigating the Landscape of Autonomous Buses: Insights in Ibaraki, Japan

Navigating the Landscape of Autonomous Buses: Insights in Ibaraki, Japan

Robust model-free adaptive iterative learning control for an autonomous bus trajectory tracking system.

A robust model-free adaptive iterative learning control (R-MFAILC) algorithm is proposed in this work to address the issue of laterally controlling an autonomous bus. First, according to the periodic repetitive working characteristics of autonomous buses, a novel dynamic linearized method used in the iterative domain is utilized, and a time-varying data model with a pseudo gradient (PG) is given. Then, the R-MFAILC controller is designed with a proposed adaptive attenuation factor. The proposed algorithm's advantage lies in the R-MFAILC controller, which solely utilizes the input and output data of the regulated entity. Moreover, the R-MFAILC controller has strong robustness and can handle the nonlinear measurement disturbances of the system. In simulations based on the Truck-Sim simulation platform, the effectiveness of the proposed algorithm is verified. A rigorous mathematical analysis is employed to demonstrate the stability and convergence of the proposed algorithm.

Factors Influencing Passengers to Use Autonomous Bus in China Cities

Factors Influencing Passengers to Use Autonomous Bus in China Cities

Kinematic analysis of an unrestrained passenger in an autonomous vehicle during emergency braking.

Analyzing human body movement is a critical aspect of biomechanical studies in road safety. While most studies have traditionally focused on assessing the head-neck system due to the restraint provided by seat belts, it is essential to examine the entire pelvis-thorax-head kinematic chain when these body regions are free to move. The absence of restraint systems is prevalent in public transport and is also being considered for future integration into autonomous vehicles operating at low speeds. This article presents an experimental study examining the movement of the pelvis, thorax and head of 18 passengers seated without seat belts during emergency braking in an autonomous bus. The movement was recorded using a video analysis system capturing 100 frames per second. Reflective markers were placed on the knee, pelvis, lumbar region, thorax, neck and head, enabling precise measurement of the movement of each body segment and the joints of the lumbar and cervical spine. Various kinematic variables, including angles, displacements, angular velocities and accelerations, were measured. The results delineate distinct phases of body movement during braking and elucidate the coordination and sequentiality of pelvis, thorax and head rotation. Additionally, the study reveals correlations between pelvic rotation, lumbar flexion, and vertical trunk movement, shedding light on their potential impact on neck compression. Notably, it is observed that the elevation of the C7 vertebra is more closely linked to pelvic tilt than lumbar flexion. Furthermore, the study identifies that the maximum angular acceleration of the head and the maximum tangential force occur during the trunk's rebound against the seatback once the vehicle comes to a complete stop. However, these forces are found to be insufficient to cause neck injury. While this study serves as a preliminary investigation, its findings underscore the need to incorporate complete trunk kinematics, particularly of the pelvis, into braking and impact studies, rather than solely focusing on the head-neck system, as is common in most research endeavors.

Feedforward-feedback-enhanced model-free adaptive iterative learning control with measurement disturbance and data dropout for an autonomous bus trajectory tracking system.

This article presents an innovative enhanced model-free adaptive iterative learning control approach suited for autonomous bus trajectory tracking systems that may experience measurement disruptions and random data dropouts. Data loss can occur independently and randomly at different times and in different iterations with varying probabilities, leading to successive data dropouts on both the time and iteration axes. The proposed enhanced model-free adaptive iterative learning control controller incorporates a data compensation mechanism to compensate for missing data, ensuring excellent control performance. This data-driven control strategy requires only input/output data for controller design. The convergence and effectiveness of the proposed approach are verified through rigorous mathematical analysis and simulation outcomes.

Motion Planning for Urban Autonomous Bus Considering Localization Uncertainty Using Offset-Free MPC

Motion Planning for Urban Autonomous Bus Considering Localization Uncertainty Using Offset-Free MPC

Analysis of the Development of Sports Intelligence From the Application of "Intelligent Robot Dogs" in Competitive Sports

The 19th Asian Games, hosted in Hangzhou, China, have been described as a large-scale event of sports intelligence, with high-tech technology such as "digital torchbearers", autonomous buses, 5G information networks, meta-universes, and intelligent robots on show. This paper will use the "intelligent robot dog" as the research object and will use the literature method, case study method, logical reasoning method, and other research methods to describe the concepts of digitalization and intelligent robots and analyse the positive effects that the combination of sports and intelligence brings to sports competitions. Using the application of an intelligent robot dog in sports competition at the Hangzhou Asian Games as a starting point, this paper will analyse the current state of sports intelligence, present the problems encountered as well as the solutions, and finally forecast the future market development of the combination of sports and artificial intelligence applied in competitive sports.

Autonomous Bus Operation Alternatives in Urban Areas Using Fuzzy Dombi-Bonferroni Operator Based Decision Making Model

Advances in V2V, V2I, and autonomous vehicle technologies have made autonomous buses possible in cities. Soon, autonomous bus operations will be common in urban areas, which will improve sustainability, safety, and the city’s technology. These buses have different operation types. Each operation has advantages and disadvantages. Therefore, the goal of this study is to serve as a guide for decision-makers during the transition to autonomous buses. Four alternatives are evaluated based on eleven criteria organized under four main aspects, namely autonomous buses for special uses, autonomous buses for last-mile uses, autonomous cars in mixed traffic, and autonomous buses in closed systems. We propose an Ordinal Priority Approach (OPA) method for determining the criteria weights and application of fuzzy Dombi Bonferroni (DOBI) methodology for the evaluation of alternatives. When compared to the other three alternatives in this study, the results show that deploying autonomous buses in mixed traffic is the most advantageous option.

Predicting acceptance of autonomous shuttle buses by personality profiles: a latent profile analysis

AbstractAutonomous driving and its acceptance are becoming increasingly important in psychological research as the application of autonomous functions and artificial intelligence in vehicles increases. In this context, potential users are increasingly considered, which is the basis for the successful establishment and use of autonomous vehicles. Numerous studies show an association between personality variables and the acceptance of autonomous vehicles. This makes it more relevant to identify potential user profiles to adapt autonomous vehicles to the potential user and the needs of the potential user groups to marketing them effectively. Our study, therefore, addressed the identification of personality profiles for potential users of autonomous vehicles (AVs). A sample of 388 subjects answered questions about their intention to use autonomous buses, their sociodemographics, and various personality variables. Latent Profile Analysis was used to identify four personality profiles that differed significantly from each other in their willingness to use AVs. In total, potential users with lower anxiety and increased self-confidence were more open toward AVs. Technology affinity as a trait also contributes to the differentiation of potential user profiles and AV acceptance. The profile solutions and the correlations with the intention to use proved to be replicable in cross validation analyses.