Reliable automated driving technology is challenged by various sources of uncertainties, in particular, behavioral uncertainties of traffic agents. It is common for traffic agents to have intentions that are unknown to others, leaving an automated driving car to reason over multiple possible behaviors. This paper formalizes a behavior planning scheme in the presence of multiple possible futures with corresponding probabilities. We present a maximum entropy formulation and show how, under certain assumptions, this allows delayed decision-making to improve safety. The general formulation is then turned into a model predictive control formulation, which is solved as a quadratic program or a set of quadratic programs. We discuss implementation details for improving computation and verify operation in simulation and on a mobile robot.

Smooth and safe speed planning is imperative for the successful deployment of autonomous vehicles. This paper presents a mathematical formulation for the optimal speed planning of autonomous driving, which has been validated in high-fidelity simulations and real-road demonstrations with practical constraints. The algorithm explores the inter-traffic gaps in the time and space domain using a breadth-first search. For each gap, quadratic programming finds an optimal speed profile, synchronizing the time and space pair along with dynamic obstacles. Qualitative and quantitative analysis in Carla is reported to discuss the smoothness and robustness of the proposed algorithm. Finally, we present a road demonstration result for urban city driving.

This article investigates the effects of visual warning presentation methods on human performance in augmented reality (AR) driving. An experimental user study was conducted in a parking lot where participants drove a test vehicle while braking for any cross traffic with assistance from AR visual warnings presented on a monoscopic and volumetric head-up display (HUD). Results showed that monoscopic displays can be as effective as volumetric displays for human performance in AR braking tasks. The experiment also demonstrated the benefits of conformal graphics, which are tightly integrated into the real world, such as their ability to guide drivers' attention and their positive consequences on driver behavior and performance. These findings suggest that conformal graphics presented via monoscopic HUDs can enhance driver performance by leveraging the effectiveness of monocular depth cues. The proposed approaches and methods can be used and further developed by future researchers and practitioners to better understand driver performance in AR as well as inform usability evaluation of future automotive AR applications.

Each year, millions of motor vehicle traffic accidents all over the world cause a large number of fatalities, injuries and significant material loss. Automated Driving (AD) has potential to drastically reduce such accidents. In this work, we focus on the technical challenges that arise from AD in urban environments. We present the overall architecture of an AD system and describe in detail the perception and planning modules. The AD system, built on a modified Acura RLX, was demonstrated in a course in GoMentum Station in California. We demonstrated autonomous handling of 4 scenarios: traffic lights, cross-traffic at intersections, construction zones and pedestrians. The AD vehicle displayed safe behavior and performed consistently in repeated demonstrations with slight variations in conditions. Overall, we completed 44 runs, encompassing 110km of automated driving with only 3 cases where the driver intervened the control of the vehicle, mostly due to error in GPS positioning. Our demonstration showed that robust and consistent behavior in urban scenarios is possible, yet more investigation is necessary for full scale rollout on public roads.

This study presents the preliminary attempt to investigate the usage of driver physiology signals, including electrocardiography (ECG) and respiration wave signals, to predict driving maneuvers. While most studies on driving maneuver prediction uses direct measurements from vehicle or road scene, we believe the mental state changes from the driver when making plans for maneuver can be reflected from the physiological signals. We extract both time and frequency domain features from the physiological signals, and use them as the features to predict the drivers' future maneuver. We formulate the prediction of driver maneuver as a multi-class classification problem by using the features extracted from signal before the driving maneuvers. The multi classes correspond to various types of driving maneuvers including Start, Stop, Lane Switch and Turn. We use the support vector machine (SVM) as the classifier, and compare the performance of using both physiological and car signals (CAN bus) with the baseline classifier that is trained with only car signal. An improved performance is observed when using the physiological features with 0.04 in F-score on average. This improvement is more obvious as the prediction is made earlier.

Current pedestrian collision warning systems use either auditory alarms or visual symbols to inform drivers. These traditional approaches cannot tell the driver where the detected pedestrians are located, which is critical for the driver to respond appropriately. To address this problem, we introduce a new driver interface taking advantage of a volumetric head-up display (HUD). In our experimental user study, sixteen participants drove a test vehicle in a parking lot while braking for crossing pedestrians using different interface designs on the HUD. Our results showed that spatial information provided by conformal graphics on the HUD resulted in not only better driver performance but also smoother braking behavior as compared to the baseline.

This paper presents a constructivist methodology oriented to training Robotics in its wide sense, and its implementation in higher education. A humanoid robotics workshop is included as a part of the curriculum of `Industrial Robotics' in the Industrial Engineering. This workshop covers one third of the laboratory practices of this subject. A constructivist view for learning is adopted, whereby robotic technologies are not seen as mere tools, but rather as potential vehicles of new ways of thinking about teaching, learning and education at large. Students, in a constructivist learning environment, are invited to work on experiments and authentic problem-solving; with selective use of available resources according to their own interests, research and learning strategies. The authors, as trainers of this workshop, chose ROBONOVA humanoid robots, among other devices, which attempt to partner technology with ideas of constructivism. The materials used in the workshop offer building parts, sensors thus connecting a robot with the external environment and programming software with a simple graphical interface intended for the creation of robot behavioral movements. The idea is “learning by design”, which is central in the constructivist pedagogy introduced firstly by Resnick. In this workshop we corroborate this idea through the project-based learning approach in robotics. Learning tasks of the workshop are organized as projects (small and large) that encourage students to develop their own designs. Projects are either instructor led or completely arisen from students.

SDSim is a novel solar dryer simulator based in a multi-crop, inclined multi-pass solar air heater with in-built thermal storage mathematical model. This model has been developed as a designing and developing tool used to study and forecast the behavior of the system model in order to improve its drying efficiency and achieving a return on the dryer investment. The main feature of this simulator is that most of the parameters are permitted to be changed during the simulation process allowing finding the more suitable system for any specific situation with a user-friendly environment. The model has been evaluated in a real solar dryer system by comparing model estimates to collected data.