OculAR: The best way to enjoy AR cars on your Android device - Download and get started
Besides, there are nearly 15 types of supercars and cars for you to test drive in this virtual world. You can accelerate, slow down, and drift on dangerous roads, and downhill at towering peaks. These suffocating polygons will make you feel more stimulated than ever.
OculAR - Drive AR Cars is an Augmented Reality (AR) app that allows users to drive a variety of different cars on the street, and even drive on the beach, with a realistically-rendered driving experience. The app provides a variety of different vehicles to drive, including a variety of different sports cars, SUVs, pickup trucks, and other cars. Users can even choose the color of their vehicles, and choose the type of tires they want to put on their vehicles.
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A fixed-base driving simulator with a partial car cab (F12PI-3/A88, Foerst GmbH, Wiehl, Germany) was used to measure simulated driving performance. The simulator car was mounted with instruments of a Ford Focus. The driving scene was projected on three projection screens (1.80 1.39 m), realizing a 180 horizontal and 40 vertical field of view . A two-lane motorway was used as a driving route. It included a straight section with two wide left-hand curves and three wide right-hand curves. The scenery around the motorway was characterized by a landscape with forests and an on-ramp. Figure 1 shows the driving simulator visual imagery during the day and night condition. The luminance levels of the different imagery under day (center screen street 30.5 cd/m2, left screen street 31.8 cd/m2, grass 22.6 cd/m2, trees 8.0 cd/m2, sky 136.5 cd/m2) and night conditions (center screen street 3.7 cd/m2, left screen street 0.79 cd/m2, grass 0.56 cd/m2, trees 0.54 cd/m2, sky 0.46 cd/m2) were recorded using a luminance meter (LS-110, Minolta Co. Ltd., Osaka, Japan). Each subject drove once under daylight condition and once under night condition in randomized order. Both driving scenes included the same route, but with the following sub-tasks in randomized order for day and night conditions: drive on a straight stretch, navigate along a narrow lane past the road repair section, three cars to overtake, and avoid collision with a parked car between the service lane and the right lane. There was no other ambient traffic to ensure a standardized driving scene for all subjects. Speed limit, indicated by street signs, was 80 km/h with the exception of 60 km/h along the roadwork section. The entire driving route was 5.9 km long and took participants five to six minutes to complete. To become familiar with the driving simulator, all participants drove a five minutes practice route on a motorway under daylight condition without any other vehicles. After this training section, three participants felt slightly discomfort most likely due to simulator sickness. They were excluded from further analysis. Participants were instructed to drive as they would normally do and follow traffic regulations and traffic signs.
OculAR is one of the most realistic Augmented reality app available on the Google Play Store for ARCore supported Android devices.OculAR lets you drive your dream car in the real world using modern AR techniques.- Ultra Realistic cars.- Realistic Vehicle Physics- Click Picture and share with friends- Perform Stunts- Do Ramp Jumps- 12+ Cars to choose Visuals of OculAR will sometimes make you believe that it's a real car, you can take pictures of cars in your world and share that with your friends.OculAR supports both Indoor and outdoor modes. Other than cars you can place ramps, tires, and many more objects to make the experience more intuitive.Download the OculAR for free and don't miss amazing NEW CONTENT on periodic UPDATES*ARCore compatible device is needed to run the AR application.
However, as vehicles become increasingly connected, independent, and even begin to drive themselves, we may see more hackers turning to cars as an area of interest. This is especially true as wireless systems make your car more vulnerable to attacks.
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As an objective method of assessing driver self-regulation practices, speed behavior in different conditions was measured using a driving simulator as in previous studies9,27,28,29. Participants drove a route of approximately 12.5 km and took approximately 15 min if they drove close to the speed limits. The simulation included three different road types with speed limits and a design similar to those found on the Spanish road network: dual carriageway, mountain road, and inner-city. Within these road types, different scenarios were chosen to analyze the influence of different road characteristics on driving speed. The scenarios had varying degrees of traffic complexity (oncoming cars, cars in the same direction), different layouts (straight, slight bends, sharp bends), and the presence/type of slope (no slope, ascending, descending). The inner-city route also included the presence of parked cars at the sides of the road. Table 1 and Fig. 1 show the characteristics for each scenario.
Some driver characteristics were also found to be significant predictors of driving speed. Although age did not have an influence, we found that gender influenced speed selection. Men drove faster than women, with a difference of 5.61 kph. Intraocular straylight in the better eye also influenced driver speed. According to the model, an increase of one unit in the log(s) of the better eye (a worse value) implied a speed reduction of 13.68 kph. Figure 2 shows the mean speeds measured in the different scenarios according to the level of straylight in the better eye. As such, it is evident that participants with a log(s) of greater than 1.4 drove more slowly in all scenarios except for scenario 3 (mountain road, straight, 90 kph SL). Other authors have proposed this value of log(s) as the cut-off value for safe driving24,32,33.
The results of visual assessments demonstrated that older drivers with cataract had significantly worse visual acuity, contrast sensitivity, and they also showed a significantly higher level of intraocular straylight, which agrees with previous studies9,24,34. This impairment is manifested in all visual parameters for both the worse and better eyes in our sample, given that most drivers with cataract had lens opacity in both eyes.
Drivers typically self-regulate their behavior to compensate for such limitations. Thus, the cataract group adopted lower speeds than the control group in all scenarios except scenario 3, a straight segment where they drove at similar speeds (mean difference of approximately 1.0 kph). This agrees with previous research in real10,35,36 and simulated driving situations9. Moreover, the difference in speed selection between the two groups was more accentuated in roads with a higher speed limit, such as scenarios 1 and 2, with a limit of 120 kph, and scenarios 4, 7, and 8, with a limit of 90 kph. Although this tendency was not observed in scenario 3, these results indicate that drivers with cataract may feel less safe on high speed roads. This is congruent with previous studies based on self-reported data collected from questionnaires and naturalistic driving data, which have shown that drivers affected by cataract often avoid driving on highways/freeways2,37 and report driving more slowly than the general traffic flow11. Driver self-regulation is present not only in drivers with cataract, but also in drivers with visual impairment due to other ocular disease. Glaucoma is one of them, and visual field loss result in driving self-restriction38. Some of the self-regulation strategies of these drivers include avoiding more demanding situations such as driving at night, in rush hour or driving in high-speed roads39,40,41. Another ocular disease that leads to driving self-regulation is age related macular degeneration (ARMD). They usually start by reducing night-time driving42,43 and continue avoiding other situations such as driving in rush hour, in high-speed roads or in adverse meteorological conditions18,43,44. The study of Szlyk et al. compared drivers with ARMD with a control group in a driving simulator and an on-road circuit. The authors found that ARMD drivers avoided unfamiliar areas or changing lanes and they adopted slower speeds than the control group43. In agreement with our results for drivers with cataract, these drivers seem to extreme caution with strategies such as reducing their speeds, memorizing the route, increasing the road scanning or using a passenger who verbalize details of the road45.
Purpose: In most states, people with reduced visual acuity may legally drive with the aid of a bioptic telescope. However, concerns have been raised that the ring scotoma may impair detection of peripheral hazards. Using a driving simulator, we tested the hypothesis that the fellow eye would be able to compensate for the ring scotoma when using a monocular telescope.
Methods: Sixteen bioptic users completed three drives with binocular viewing interleaved between three drives with monocular viewing. Forty pedestrians appeared and ran on the road for 1 second, including 26 within the ring scotoma, while participants were reading road signs through their own monocular telescopes. Head movements were analyzed to determine whether the pedestrian appeared before or only while using the telescope.
Results: For pedestrians that appeared only during bioptic use and were likely in the area of the ring scotoma, detection rates were significantly higher in binocular (fellow eye can compensate) than monocular (fellow eye patched) viewing (69% vs. 32%; P
The first step in nearly every application of eye tracking is segmenting the gaze positions into sequences of movement types, such as fixations and saccades. These segments are then available for interpretation in terms of cognitive task manipulations, natural behavior strategies, or clinically diagnostic behaviors, depending on the research motivating the data collection. The depth and quality of inferences drawn from these segments are clearly affected by the quality of the gaze segmentation process and the assumptions underlying that process. We propose that initial eye-tracking data parsing can be augmented using an unsupervised learning approach in which the ocular dynamics represented in the data drive the event identification process. Unsupervised parsing offers an alternative, exploratory data analysis step that can occur in parallel with or as an alternative to a more traditional initial gaze segmentation process. The latter might be thought of as parsing driven by the assumptions of the investigators about movement characteristics, where the unsupervised parsing is driven by the data characteristics. In this paper, we demonstrate the use of a state-of-the art machine learning algorithm, based on the combination of a beta process, a vector auto-regressive process, and a hidden Markov model (henceforth abbreviated as BP-AR-HMM) developed by Fox and colleagues (Fox, 2009; Fox, Sudderth, Jordan, & Willsky, 2011; Fox, Hughes, Sudderth, & Jordan, 2014).