Search Results (6)

Aims: To investigate the road safety concerns associated with pet dogs in New Zealand. Methods: An online survey was developed and offered to New Zealanders via SurveyMonkey© from 8 January 2019 to 31 March 2019) using New Zealand residents aged ≥ 18 years. Questions about the demographics of respondents and their number of pet dogs along with those about dog transportation and dog roaming were asked. Results: Of 2744 respondents who completed the online survey, 1494 (65%) owned a dog. Of the total respondents, 1511 completed the question about how their dog or dogs travel in vehicles. Of these, 2% (n = 29) let their dog sit on the driver’s knee, and 12% (n = 179) allowed their dog to sit on the passenger’s knee. Only 7% (n = 105) allowed the dog to roam free throughout the vehicle, while 44% (n = 663) allowed their dogs to roam free in the backseat area only and 19% (n = 280) put their dog in the boot/hatchback area. A minority of respondents (2%, n = 22) indicated they let their dog travel unrestrained on the deck of a ute or truck. Approximately half, 51% (n = 767), of the respondents indicated that they restrained or crated their dogs in or on the vehicle. Respondents also expressed concerns about roaming dogs being a road safety hazard with the themes of responsible ownership, physical and psychological harm, and the dangers of rescue altruism emerging. Conclusions: Increased awareness about proper pet restraints during transportation and preventing dogs from roaming, especially near and on roads, is crucial to ensure traffic safety for humans and dogs. Full article

The primary objectives of this paper are to test an adaptive sampling method for an autonomous underwater vehicle, specifically tailored to track a hydrocarbon plume in the water column. An overview of the simulation of the developed applications within the autonomous system is presented together with the subsequent validation achieved through field trials in an area of natural oil seeps near to Scott Inlet in Baffin Bay. This builds upon our prior published work in methodological development. The method employed involves an integrated backseat drive of the AUV, which processes in situ sensor data in real time, assesses mission status, and determines the next task. The core of the developed system comprises three modular components—Search, Survey, and Sample—each designed for independent and sequential execution. Results from tests in Baffin Bay demonstrate that the backseat drive operating system successfully accomplished mission goals, recovering water samples at depths of 20 m, 50 m, and 200 m before mission completion and vehicle retrieval. The principal conclusion drawn from these trials underscores the system’s resilience in enhanced decision autonomy and validates its applicability to marine pollutant assessment and mitigation. Full article

Autonomous underwater vehicles (AUVs) have been applied in various scientific missions including oceanographic research, bathymetry studies, sea mine detection, and marine pollution tracking. We have designed and field-tested in the ocean a backseat driver autonomous system for a 5.5 m survey-class Explorer AUV to detect and track a mixed-phase oil plume. While the first driver is responsible for controlling and safely operating the vehicle; the second driver processes real-time data surrounding the vehicle based on in situ sensor measurements and adaptively modifies the mission details. This adaptive sensing and tracking method uses the Gaussian blur and occupancy grid method. Using a large bubble plume as a proxy, our approach enables real-time adaptive modifications to the AUV’s mission details, and field tests show successful plume detection and tracking. Our results provide for remote detection of underwater oil plumes and enhanced autonomy with these large AUVs. Full article

Car interiors are envisioned to be living spaces that support a variety of non-driving-related activities. Previous work focuses on enhancing driving-related functions, performance and safety. By developing textile-based sensors, we focus on enabling non-driving activities integrated in the car interior and supporting a richer user experience. In this paper, we introduce an array of new applications using e-textile sensors to the design space of car interiors. Our functional prototypes implement hand interactions (such as press and double tap gestures) on the leather or fabric of the steering wheel and back of the head rest. We then propose applications for these sensors to control media, car windows, and air-conditioning. Overall, the paper contributes a novel tactile input modality to support drivers and empower backseat passengers. Full article

Adaptive sampling provides an innovative and favorable method of improving the effectiveness of underwater vehicles in collecting data. Adaptive sampling works by controlling an underwater vehicle by using measurements from sensors and states of the vehicle. A backseat driver system was developed in this work and installed on a Slocum glider to equip it with an ability to perform adaptive sampling tasks underwater. This backseat driver communicated with the main vehicle control system of the glider through a robot operating system (ROS) interface. The external control algorithms were implemented through ROS nodes, which subscribed simulated sensor measurements and states of the glider and published desired states to the glider. The glider was set up in simulation mode to test the performance of the backseat driver as integrated into the control architecture of the glider. Results from the tests revealed that the backseat driver could effectively instruct the depth, heading, and waypoints as well as activate or deactivate behaviors adaptively. The developed backseat driver will be tested in future field experiments with sensors included and safety rules implemented before being applied in adaptive sampling missions such as adaptive oil spill sampling. Full article

We introduce an adaptive sampling method that has been developed to support the Backseat Driver control architecture of the Memorial University of Newfoundland (MUN) Explorer autonomous underwater vehicle (AUV). The design is based on an acoustic detection and in-situ analysis program that allows an AUV to perform automatic detection and autonomous tracking of an oil plume. The method contains acoustic image acquisition, autonomous triggering, and thresholding in the search stage. A new biomimetic search pattern, the bumblebee flight path, was designed to maximize the spatial coverage in the oil plume detection phase. The effectiveness of the developed algorithm was validated through simulations using a two-dimensional planar plume model and a 90-degree scanning sensor model. The results demonstrate that the bumblebee search design combined with a genetic solution for the Traveling Salesperson Problem outperformed a conventional lawnmower survey, reducing the AUV travel distance by up to 75.3%. Our plume detection strategy, using acoustic sensing, provided data of plume location, distribution, and density, over a sector in contrast with traditional chemical oil sensors that only provide readings at a point. Full article