Extreme Car Driving Simulator Beta Mod Apk
Graciana Prater
Extreme Car Driving Simulator is a cool sports car racing game where players are thrust into the exhilarating world of high-speed racing and precision driving. You can play this game online and for free on Silvergames.com. With its stunning 3D graphics and immersive gameplay, this game offers an unparalleled experience for enthusiasts of extreme driving. As a player, you have the opportunity to showcase your skills behind the wheel of powerful drift cars, engaging in adrenaline-pumping drag races and heart-stopping drift maneuvers.
One of the standout features of Extreme Car Driving Simulator is its realistic physics engine, which accurately simulates the dynamics of each vehicle, allowing for precise control and responsive handling. Whether you're executing hairpin turns, drifting around corners, or accelerating down straightaways, you'll feel the thrill of the open road as you push the limits of your driving abilities.
As you progress through the game, you'll have the chance to unlock and customize a variety of drift cars, each with its own unique characteristics and performance attributes. Whether you prefer sleek sports cars or rugged muscle cars, there's a vehicle to suit every taste and driving style. With its dynamic gameplay, stunning visuals, and extensive customization options, Extreme Car Driving Simulator offers an immersive and thrilling experience that will keep players coming back for more. So buckle up, hit the gas, and prepare to unleash your inner drift master in this adrenaline-fueled racing adventure! Have fun playing this Extreme Car Driving Simulator!
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One of the hallmarks of being human is our unique ability to develop skills and expertise. While all animals develop skills like walking, running, fruit picking or hunting, we as humans can develop a much broader and more diverse set of skills. With practice, most of us can learn to play a musical instrument, play a sport, or do arts and crafts. Nevertheless, only some of us can reach the highest level of expertise. Unlike the widespread view that this is entirely driven by practice1, there is accumulating evidence that practice is not enough2, making individual musicians, artists, athletes, and craftspeople who take this expertise to new heights of particular research interest. Novel technology for mobile brain and body imaging now enables us to study neurobehaviour in real-world settings3,4,5. When carried out in natural environments, these measures can enable a meaningful understanding of human behaviour while performing real-life tasks. Studying the relation and inter-dependencies between brain activity and body movements of experts, while they perform their expert skills in real-world settings, can enable us to unpack this enigma.
The experiment took place at the Dunsfold Aerodrome (Surrey, UK), commonly known as the Top Gear race track. The driver (co-author) was Formula E champion, Lucas Di Grassi (Audi Sport ABT Schaeffler team), with over 15 years of professional racing experience, which include karting, Formula 3, Formula One and Formula E racing. The participant, LDG, is an author on this paper and gave informed consent to participate in the experiment and to publish the information and images in an online open-access publication. While all methods used in the study were approved by the Imperial College Research Ethics Committee and performed in accordance with the declaration of Helsinki, this study was a self-experimentation by an author17. The test drive was prescheduled for video production purposes by the racing team, who race in these conditions frequently. It enabled us this unique scientific observation of motor expertise in the wild. Although unlikely to be needed, emergency response units were present. A promo video of the film by Averner Films18 is accessible here:
The driver was equipped with: a 32-channels wireless EEG system (LiveAmp, Brain Products GmbH, Germany) with dry electrodes (actiCAP Xpress Twist, Brain Products GmbH, Germany); binocular eye-tracking glasses (SMI ETG 2W A, SensoMotoric Instruments, Germany); and four inertial measurement units (IMUs) on his hands and feet (MTw Awinda, Xsens Technologies BV, The Netherlands); shown in Fig. 1A. The car was equipped with a GPS and a camera recording the inside of the car. The car driver assistance systems were turned off. The full architecture of the experimental setup is presented in Fig. 1C.
The results section of this case-study paper were written to characterise the neuromotor behaviour of a professional driver while driving in extreme condition, which can be used as a reference point for future driving studies. Initial analysis was aimed to understand the interdependencies between the different neurobehavioural data streams and their level of complexity. The analysis then focused on specific driving events, such as response to challenging conditions (skidding, curves and straights), in order to assess if there is a distinguishable behaviour upon those moments. Lastly, we addressed the causality across different data streams.
Histogram evolution analysis detail the extreme driving scenario of this experiment. (top) car speed with an average of 120Km/h, critical curve speed average of 78 Km/h and straight speed average of 130 Km/h, all above conventional driving speed limits; (middle) right-hand gyroscope with an average value of 0.9 rad/s whereas for intense driving style (critical curve and skidding moments) the average values are of 1 and 3 rad/s, superior to normal movement expected from literature; and (bottom) right-hand accelerometer data showing absolute acceleration with similar results distribution as gyroscope data. Data regarding straights corresponds to 25.5% of the entire data set and the Hammerhead curve to 11.3%.
The distributions of the car speed, the right-hand rotation, and the right-hand accelerations were assessed in the entire track, the straight segments before and after the hammerhead curve, and the hammerhead curve itself (Fig. 2). The average speed throughout the experiment was 120Km/h. As expected, in the curves speed was relatively low (between 54 and 82 Km/h), while in straight segments speed was much higher with broader distribution, as the car decelerated towards a curve and accelerated after the curve. The number of frames considered hammerhead critical curve was 11.3% of the total recording, and straights corresponded to 25.5%.
Since the hand movements were highly correlated here, we show only the right hand. Both gyroscope and accelerometer distributions present similar tendencies, with a narrow distribution during the straight segments and a slightly wider one in curves. The result considering the whole data set lies in-between. The gyroscope values for the abrupt responses (skidding) have a mean of 3rad/s, considerably superior to the 1rad/s found in the literature for normal forearm movement32, which is expected considering the intense car handling. Data considered as abrupt responses corresponds to 6% of the dataset.
Eye gaze data showed a strong tendency of tracking the tangent point of the curve, as illustrated in Fig. 3 (top), and reported for non-expert drivers33. In the top figure the eye gaze search for the tangent of the curve can be seen (internal and external), marked in white on the road, where it remains throughout the entire curve. During straight segments, the eye gaze focuses straight ahead, with a stable distance in the horizon, with minor saccadic deviations, as illustrated in Fig. 3 (bottom). The heat map was built using data recorded during the critical curve (top) or the straights before and after that curve (bottom). The gaze point position from the geocentric view was annotated at a ten frames cadence in an overlapping position matrix. The heat map was built resorting to the percentual annotations occurrence in this matrix.
Our results show changes in the EEG power and the gaze characteristic during sharp curves, where the control of the car is most challenging. While many previous studies found correlations between hand movements and brain activity in lab-based repeated trials tasks37, here we show such correlations in continuous movement in-the-wild. Moreover, in a controlled lab experiment, there is a clear trial order where the timing of stimuli appearance, go-cue, etc. are well defined. Accordingly, the direction of the causality (if it exists) is clear -neural activity after a stimulus can be caused by it but cannot cause it. At the same time, neural activity before movement can cause the movement but cannot be caused by it. In-the-wild, causal relationships may reverse or be bi-directional. Here we show not only the correlation but the causality from the brain activity to the body movement in an unconstrained setting. Interestingly, the EEG power changes are in line with previous results on general creative solution finding and interventions38.
Neurobehavioural data collection in-the-wild is subject to more noise sources and interference than standard data collection in-the-lab. This concern is specifically worrying for the EEG signal, which is always contaminated by noise, and any EEG recording during movement is subject to movement and muscle artefacts. Thus, we find our cross-correlation and Granger causality results very encouraging, as those suggest the EEG activity precedes the movement and predicted it and not the other way around. If the EEG results were simply movement artefacts, we would have expected to see the opposite causality - the movement would precede and predict the EEG movement artefact. Thus, since the EEG activity precedes the movement, we believe the EEG results cannot be rejected as noise artefacts.
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