Evaluation of Imminent Take-Over Requests With Real Automation on a Test Track

Objective Investigating take-over, driving, non-driving related task (NDRT) performance, and trust of conditionally automated vehicles (AVs) in critical transitions on a test track. Background Most experimental results addressing driver take-over were obtained in simulators. The presented experiment aimed at validating relevant findings while uncovering potential effects of motion cues and real risk. Method Twenty-two participants responded to four critical transitions on a test track. Non-driving related task modality (reading on a handheld device vs. auditory) and take-over timing (cognitive load) were varied on two levels. We evaluated take-over and NDRT performance as well as gaze behavior. Further, trust and workload were assessed with scales and interviews. Results Reaction times were significantly faster than in simulator studies. Further, reaction times were only barely affected by varying visual, physical, or cognitive load. Post-take-over control was significantly degraded with the handheld device. Experiencing the system reduced participants’ distrust, and distrusting participants monitored the system longer and more frequently. NDRTs on a handheld device resulted in more safety-critical situations. Conclusion The results confirm that take-over performance is mainly influenced by visual-cognitive load, while physical load did not significantly affect responses. Future take-over request (TOR) studies may investigate situation awareness and post-take-over control rather than reaction times only. Trust and distrust can be considered as different dimensions in AV research. Application Conditionally AVs should offer dedicated interfaces for NDRTs to provide an alternative to using nomadic devices. These interfaces should be designed in a way to maintain drivers’ situation awareness. Précis This paper presents a test track experiment addressing conditionally automated driving systems. Twenty-two participants responded to critical TORs, where we varied NDRT modality and take-over timing. In addition, we assessed trust and workload with standardized scales and interviews.

Pedestrians’ perceptions of automated vehicle movements and light-based eHMIs in real world conditions: A test track study

The development of increasingly automated vehicles (AVs) is likely to lead to new challenges around how they will interact with other road users. In the future, it is envisaged that AVs, manually driven vehicles, and vulnerable road users such as cyclists and pedestrians will need to share the road environment and interact with one another. This paper presents a test track study, funded by the H2020 interACT project, investigating pedestrians’ reactions towards an AV’s movement patterns and external Human Machine Interfaces (eHMIs). Twenty participants, standing on the side of a test-track road and facing an approaching AV, were asked to raise their arm to indicate: (1) when they could perceive the AV’s eHMI, which consisted of either a Full Light Band (FLB) or a Partial Light Band (PLB); (2) when they perceived the deceleration of the AV (with eHMI vs. no eHMI); and (3) when they felt safe to cross the road in front of the approaching AV (with eHMI vs. no eHMI). Statistical analyses revealed no effects of the presence of an eHMI on the pedestrians’ crossing decision or deceleration perception, but significant differences were found regarding the visibility of the FLB and PLB designs. The PLB design could be perceived at further distances than the FLB design. Both eHMI solutions were generally well-received, and participants provided high ratings of acceptance, perceived safety, and confidence around the AV.

High Friction Surface Treatment Deterioration Analysis and Characteristics Study

High friction surface treatment (HFST) is used to improve friction on curved roadways, especially on curves that have a history of wet pavement crashes. Observations on the long-term performance monitoring of HFST sections at the National Center for Asphalt Technology (NCAT) Test Track showed friction (skid number, SN) dropped significantly at the end of service life of HFST, creating unsafe driving conditions. There is no clear, observed friction deterioration trend to predict the friction drop when using friction performance measures like SN. Therefore, there is an urgent need to explore and develop supplementary HFST safety performance measures (such as aggregate loss) that can correlate to friction deterioration and provide predictable, cost-effective, and easily measurable results. The objectives of this paper are to (i) analyze the correlation between HFST aggregate loss percentage area and friction value using a dynamic friction tester (DFT), and (ii) study the characteristics of HFST deterioration associated with aggregate loss, at the NCAT Test Track and at selected HFST curve sites in Georgia (using 2D imaging and high-resolution 3D laser scanning). Results show a strong correlation between HFST aggregate loss percentage area and DFT friction coefficient. Where friction measurement is used as the primary safety performance measure, it is recommended that HFST aggregate loss be used as a supplementary performance measure for monitoring the HFST safety performance deterioration. Aggregate loss can be easily identified by characteristics such as color and texture change. Preliminary texture analyses of 3D HFST surface profiles show lower mean profile depth (MPD) and ridge-to-valley depth (RVD) texture indicators can also identify loss of aggregate spots on HFST surface.

Three-Dimensional Two-Phase Flow Simulations of Water Braking Phenomena for High-Speed Test Track Sled

Specializing in high-speed testing, Holloman High-Speed Test Track (HHSTT) uses a process called ‘water braking’ as a method to bring vehicles at the test track to a stop. This method takes advantage of the higher density of water, compared to air, to increase braking capability through momentum exchange. By studying water braking using Computational Fluid Dynamics (CFD), forces acting on track vehicles can be approximated and prepared for prior to actual test. In this study, focus will be made on the brake component of the track sled that is responsible for interacting with the water for braking. By discretizing a volume space around our brake, we accelerate water and air to relatively simulate the brake engaging. The model is a multi-phase flow that uses the governing equations of gas and liquid phases with the finite volume method, to perform 3D simulations. By adjusting the inflow velocity of air and water, it is possible to simulate HHSTT sled tests at various operational speeds. In the development of the 3D predictive model, convergence issues associated with the numerical mesh, initial/boundary conditions, and compressibility of the fluids were encountered. Once resolved, the effect of inflow velocities of water and air on the braking of the sled are studied.

Utilization of Cold Central Plant Recycled Asphalt in Long-Life Flexible Pavements

Long-life flexible pavements are well documented and used widely across the U.S. Found in every climate zone and traffic classification, long-life pavements do not experience deep structural distresses such as bottom-up fatigue cracking or substructure rutting. Full-scale test sections, built in 2003 at the National Center for Asphalt Technology (NCAT) Test Track, provided the basis for an optimized design approach that utilizes strain distributions for long-life thickness design. These sections, containing only virgin materials, were subjected to 30 million standard axle loadings with excellent performance in terms of rutting, cracking, and roughness. In 2012, three new sections were built at the Test Track using cold central plant recycled asphalt materials as the base layer. These layers, made from nearly 100% reclaimed asphalt pavement (RAP), supported hot mix asphalt layers that also included RAP with one section featuring in-place stabilization of the existing aggregate base. This paper provides a direct comparison between the sets of sections to compare and contrast their performance histories and structural characterization, and consider their economic and environmental impacts. None of the recycled sections are exhibiting any surface deterioration, despite heavy trafficking, and the section with a stabilized base is exhibiting lower strains than established long-life pavement thresholds. The economic analysis suggested that the recycled sections can deliver similar performance at a lower average structure normalized section cost than the non-recycled sections. Furthermore, the section with the stabilized base and 76% recycled material is likely a long-life pavement and can potentially outperform the sections with no recycled content.

The Effects of Image Augmentation on Efficiency of a Convolutional Neural Network of a Self-Driving Car

The objective of this paper was to study the effectiveness of image augmentation techniques in training a Convolutional Neural Network (CNN) of a self-driving car and identify the most suitable form of image augmentation technique, using the Udacity Car Simulator. Firstly, a dataset of augmented and non-augmented images from a training track, consisting of left-, right-, and front-facing views from the car cameras was created. Various image augmentation techniques were used: zoom, brightness, pan, flip, random (augments the image by arbitrarily choosing a technique from the previous four), and no augmentation. Secondly, training datasets consisting of the aforementioned images and a log of car turning angles, throttle, and brake were built. The final training datasets were then used with NVIDIA training method to train different CNN. The different trained networks generated steering commands from the front-facing camera of the simulation and test track had no effect on the generalization of the CNN. Lastly, different trained networks were used on the test track of Udacity Car Simulator to calculate the following variables: distance travelled, and number of crashes made by the car. After these values were acquired, an efficiency analysis was performed. The results suggested augmentation of training data is a crucial factor when it comes to the process of generalizing a model to perform tasks. Random augmentations performed the best, but a combination of flip and brightness augmentations performed equally efficiently.

Autonomous driving in adverse winter weather conditions

<p>Autonomous driving can be challenging especially in winter conditions when road surface is covered by icy and snow or visibility is low due to precipitation, fog or blowing snow. These harsh weather and road conditions set up very important requirements for the guidance systems of autonomous cars. In the normal conditions autonomous cars can drive without limitations but otherwise the speed must be reduced, and the safety distances increased to ensure safety on the roads. </p><p>Autonomous driving needs very precise and real-time weather and road condition information. Data can be collected from different sources, like (road) weather models, fixed road weather station network, weather radars and vehicle sensors (for example Lidars, radars and dashboard cameras). By combining the all relevant weather and road condition information a weather-based autonomous driving mode system is developed to help and guide autonomous driving. The driving mode system is dividing the driving conditions from perfect conditions to very poor conditions. In between there are several steps with slightly alternate driving modes depending for example snow intensity and friction. In the most challenging weather conditions, automatic driving must be stopped because the sensors guiding the driving are disturbed by for example heavy snowfall or icy road.</p><p>Finnish Meteorological Institute is testing autonomous driving in the Arctic vehicular test track in Sodankylä, Northern Finland. The test track is equipped with road weather observation system network including road weather stations, IoT sensors measuring air temperature and humidity along with various communication systems. Also, tailored road weather services are produced to the test track, like precise road weather model calculations and very accurate radar precipitation observations and nowcasting. The developed weather-based autonomous driving system is tested on Sodankylä test track among other arctic autonomous driving testing.</p><p>This study presents the Sodankylä Arctic vehicular test track environment and weather-based autonomous driving mode system that is developed at the Finnish Meteorological Institute.</p>