Toward Human-Like Automated Driving: Learning Spacing Profiles From Human Driving Data

For automated driving vehicles to be accepted by their users and safely integrate with traffic involving human drivers, they need to act and behave like human drivers. This not only involves understanding how the human driver or occupant in the automated vehicle expects their vehicle to operate, but also involves how other road users perceive the automated vehicle’s intentions. This research aimed at learning how drivers space themselves while driving around other vehicles. It is shown that an optimized lane change maneuver does create a solution that is much different than what a human would do. There is a need to learn complex driving preferences from studying human drivers. This research fills the gap in terms of learning human driving styles by providing an example of learned behavior (vehicle spacing) and the needed framework for encapsulating the learned data. A complete framework from problem formulation to data gathering and learning from human driving data was formulated as part of this research. On-road vehicle data were gathered while a human driver drove a vehicle. The driver was asked to make lane changes for stationary vehicles in his path with various road curvature conditions and speeds. The gathered data, as well as Learning from Demonstration techniques, were used in formulating the spacing profile as a lane change maneuver. A concise feature set from captured data was identified to strongly represent a driver’s spacing profile and a model was developed. The learned model represented the driver’s spacing profile from stationary vehicles within acceptable statistical tolerance. This work provides a methodology for many other scenarios from which human-like driving style and related parameters can be learned and applied to automated vehicle

Retracing trajectories: the embodied experience of cycling, urban sensescapes and the commute between ‘neighbourhood’ and ‘city’ in Utrecht, NL

This paper looks into the experience of “passing through different territories of the city” (Sennett, 2006, p. 3). Despite their importance for making sense of the city as a whole, these experiences are often not acknowledged in urban planning. This paper compares the everyday, embodied experiences of commuter cyclists with the planners’ perspective on Utrecht. ‘On the ground’ data was collected via ride-alongs with 15 inhabitants of the Leidsche Rijn neighbourhood. Our analysis reveals cycling trajectories composed of diverse sensescapes. It paints a much more complex picture of intra-urban divisions and connections than the planners’ perspective of the ‘new’ Leidsche Rijn neighbourhood separated from the ‘old’ city by major infrastructure lines

Insights into Simulated Smart Mobility on Roundabouts: Achievements, Lessons Learned, and Steps Ahead

This paper explores the domain of intelligent transportation systems, specifically focusing on roundabouts as potential solutions in the context of smart mobility. Roundabouts offer a safer and more efficient driving environment compared to other intersections, thanks to their curvilinear trajectories promoting speed control and lower vehicular speeds for traffic calming. The synthesis review supported the authors in presenting current knowledge and emerging needs in roundabout design and evaluation. A focused examination of the models and methods used to assess safety and operational performance of roundabout systems was necessary. This is particularly relevant in light of new challenges posed by the automotive market and the influence of vehicle-to-vehicle communication on the conceptualization and design of this road infrastructure. Two case studies of roundabouts were analyzed in Aimsun to simulate the increasing market penetration rates of connected and autonomous vehicles (CAVs) and their traffic impacts. Through microscopic traffic simulation, the research evaluated safety and performance efficiency advancements in roundabouts. The paper concludes by outlining areas for further research and evolving perspectives on the role of roundabouts in the transition toward connected and autonomous vehicles and infrastructures

Vehicle and Traffic Safety

The book is devoted to contemporary issues regarding the safety of motor vehicles and road traffic. It presents the achievements of scientists, specialists, and industry representatives in the following selected areas of road transport safety and automotive engineering: active and passive vehicle safety, vehicle dynamics and stability, testing of vehicles (and their assemblies), including electric cars as well as autonomous vehicles. Selected issues from the area of accident analysis and reconstruction are discussed. The impact on road safety of aspects such as traffic control systems, road infrastructure, and human factors is also considered

Seamless Interactions Between Humans and Mobility Systems

As mobility systems, including vehicles and roadside infrastructure, enter a period of rapid and profound change, it is important to enhance interactions between people and mobility systems. Seamless human—mobility system interactions can promote widespread deployment of engaging applications, which are crucial for driving safety and efficiency. The ever-increasing penetration rate of ubiquitous computing devices, such as smartphones and wearable devices, can facilitate realization of this goal. Although researchers and developers have attempted to adapt ubiquitous sensors for mobility applications (e.g., navigation apps), these solutions often suffer from limited usability and can be risk-prone. The root causes of these limitations include the low sensing modality and limited computational power available in ubiquitous computing devices. We address these challenges by developing and demonstrating that novel sensing techniques and machine learning can be applied to extract essential, safety-critical information from drivers natural driving behavior, even actions as subtle as steering maneuvers (e.g., left-/righthand turns and lane changes). We first show how ubiquitous sensors can be used to detect steering maneuvers regardless of disturbances to sensing devices. Next, by focusing on turning maneuvers, we characterize drivers driving patterns using a quantifiable metric. Then, we demonstrate how microscopic analyses of crowdsourced ubiquitous sensory data can be used to infer critical macroscopic contextual information, such as risks present at road intersections. Finally, we use ubiquitous sensors to profile a driver’s behavioral patterns on a large scale; such sensors are found to be essential to the analysis and improvement of drivers driving behavior.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163127/1/chendy_1.pd

Advanced Sensing and Control for Connected and Automated Vehicles

Connected and automated vehicles (CAVs) are a transformative technology that is expected to change and improve the safety and efficiency of mobility. As the main functional components of CAVs, advanced sensing technologies and control algorithms, which gather environmental information, process data, and control vehicle motion, are of great importance. The development of novel sensing technologies for CAVs has become a hotspot in recent years. Thanks to improved sensing technologies, CAVs are able to interpret sensory information to further detect obstacles, localize their positions, navigate themselves, and interact with other surrounding vehicles in the dynamic environment. Furthermore, leveraging computer vision and other sensing methods, in-cabin humans’ body activities, facial emotions, and even mental states can also be recognized. Therefore, the aim of this Special Issue has been to gather contributions that illustrate the interest in the sensing and control of CAVs

The Potential for Autonomous Vehicle Technologies to Address Barriers to Driving for Individuals with Autism

Individuals with autism represent a sizeable share of the U.S. population (almost 2%), and nearly half of those with autism have average to high levels of intelligence. However, available research shows that adults with autism have a much more difficult time becoming employed and living independently compared to both typically developing adults and adults with disabilities. This study reviews the available literature on the magnitude of challenges to driving and accessing essential opportunities for adults with autism, and the potential of autonomous vehicles to address those challenges. This study is unique in that it identifies the specific driving challenges and needs faced by those with autism. The study makes the following recommendations: (1) Occupational therapists certified for driving rehabilitation should evaluate the driving abilities of those with autism and provide enhanced driver training, with and without autonomous vehicle technologies (i.e., warning systems, steering, acceleration/deceleration, and braking systems) to address any driving challenges; (2) If autonomous vehicle technology is shown in (1) to be necessary to allow for safe driving, then public funding should be made available to help with its purchase, just as funding is currently made available for those with physical disabilities to modify vehicles with adaptive equipment; (3) More tests of high automation should be conducted to affordably expand transit access; however, in the interim, public funding should be made available to subsidize ride-hailing services when transit is not a feasible travel option for those with autism; and (4) More research is needed to evaluate the effectiveness of autonomous vehicle technology interventions for driving (in 1) and expanding transit access (in 3)

The role of visual information in the steering behaviour of young and adult bicyclists

In a first series of experiments, the visual behaviour during different steering tasks, and under different constraints, was investigated in an indoor environment. Young learner, and experienced adult bicyclists were asked to steer through narrow lanes, a curved lane, and a slalom. Participants directed their gaze to the future path about one to two seconds ahead, and moved forward using optokinetic nystagmus-like eye movements. Both cycling speed and task demand were found to affect the visual behaviour of bicyclists. Although these shifts of visual attention were in line with earlier findings in pedestrians and car drivers, they did not seem to be entirely in line with the two-level model of steering behaviour. Therefore, a redefined version of this model was proposed as the ‘gaze constraints model for steering’. During a simple linear steering task, the visual behaviour of children (between 6 and 12 years of age) was similar to that of adults. However, in a more demanding slalom task children adopted a different visual-motor strategy. Whereas adults made more use of anticipatory fixations and often looked at the functional space between two cones, children mainly focussed on the upcoming cone. These findings suggest that adults plan their route through the slalom whereas children focus on steering around one cone at the time. In a second series of experiments, the distribution of visual attention was investigated in an actual traffic environment and the influence of a low quality cycling track on visual behaviour was studied. Results showed that children direct their gaze more to the environment and less to the path than adults. However, both adults and children made an apparent shift of visual attention from distant environmental regions towards more proximate road properties on the low quality cycling track. In general, the current thesis provides insights into how visual attention of young and adult bicyclists is distributed during different steering tasks and how this is affected by individual, task, and environmental constraints. Based on the current results, a gaze constraints model for steering was proposed. Furthermore, it seems that children adapted their visual behaviour to their limited capabilities, but that children’s visual behaviour changes in a similar way to changing task constraints as the visual behaviour of adults. These findings suggest that traffic rules, road infrastructure and traffic education should take into account the limited capabilities of children. However, it should be noted that this work only focussed on the lane-keeping task. Future research should therefore study the integration of these findings in the visual control of other traffic tasks such as hazard perception. A better understanding of the development of information processing of young learner bicyclists could potentially lead to better traffic education and more appropriate road infrastructure. Additionally, a new fixation-by-fixation analysis method to analyze head-mounted eye tracking data was tested in this thesis. This method was found to be a good alternative to the time-consuming frame-by-frame method, provided that the areas of interest were large, and the analysis is done over an extended period of time

Comparative analysis of MPC controllers applied to Autonomous Driving

Este trabajo presenta el diseño de un sistema de evasión de obstáculos, aplicable en situaciones de emergencia. La solución propone un MPC multivariable para controlar la posición, orientación y velocidad del vehículo autónomo. El controlador considera las limitaciones físicas del vehículo, así como la morfología de la vía para conseguir minimizar los posibles daños que puedan afectar al sistema y en consecuencia a la pérdida de control del vehículo. Las restricciones principales están basadas en las fuerzas laterales que afectan a los neumáticos, obtenidas de la implementación de los modelos cinemático y dinámico de la planta. Inicialmente, el controlador hace que el sistema siga una trayectoria predefinida. No obstante, tomará las acciones de evasión necesarias cuando detecte obstáculos, para conseguir realizar trayectorias libres de colisiones. Los resultados obtenidos tras la validación del sistema se presentan con el simulador para conducción autónoma CARLA.This work presents the design of an obstacle avoidance system, employable in emergency situations. The solution proposes a multivariable Model Predictive Controller (MPC) to control the position, orientation and velocity of an autonomous vehicle. The controller considers the vehicle0s physical limitations, as well as the road morphology, to minimize any possible damage to the system and the loss of control of the vehicle. Its main constraints are based on the lateral tire forces, obtained from the implementation of a kinematic and dynamic plant model. The controller, initially following a predefined trajectory, will take the needed evasive actions in order to perform a collision-free trajectory, in case of an obstacle detection. The results obtained from the system validation are presented with CARLA open-source simulator for autonomous driving.Grado en Ingeniería en Electrónica y Automática Industria