![]() ![]() Considering the various disturbance types, it is difficult to address this issue at the perceptual aspect. Traditional navigation methods tend to avoid every detected objects for safety, however, avoiding a ghost object may lead the vehicle into a even more dangerous situation, such as a sudden break on the highway. Existing researches and testings have shown that they can severely influence the vehicles perception ability and performance, one of the main issue is the false positive detection, i.e., the ghost object which is not real existed or occurs in the wrong position (such as a non-existent vehicle). Įnvironmental disturbances, such as sensor data noises, various lighting conditions, challenging weathers and external adversarial perturbations, are inevitable in real self-driving applications. At the time of writing, the dataset contains 60 hours of driving time. 3 shows one of our driving route and some image samples of the driving scenes by our front-facing camera, from which one can see the diverse road types (urban road, mountainous road, and highway), and the illumination changes. This reduces the chance of generating a biased dataset with many 'repetitive' scenarios, and thus allowing for an accurate judgment of the performance of the driving models. Inspired by how a driving instructor teaches a human apprentice to drive, we chose the routes and the driving time with the aim to maximize the opportunity of exposing to all typical driving scenarios. We focus on delivering realistic dataset for training driving models. Drive360 is recorded by driving in (around) multiple cities in Switzerland. With the sensors described, we collect a new dataset Drive360. #Msts cambrian route drivers#For instance, the mean reaction times for unexpected and expected human drivers are 1.3 and 0.7 s. This is also tolerable as human drivers issue driving actions at a relative low rate. The synchronization error of the CAN bus signal to our video frames is up to 10 ms. This is acceptable, as the planned route (regardless of its repre- sentation) is only needed to provide a global view for navigation. The synchronization error of the maps to our video frame is up to 16.6 ms. Our cameras, however, provide good photometric image quality and high frame rates, at the price of moderate synchronization error. We acknowledge that a camera which can be triggered by ac- curate trigger signals are preferable with respect to synchronization error. If the vehicle is at a speed of 100 km/h, the error due to the synchronization for vehicle's longitudinal position is about 23 cm. The resulting synchronization error for the video frames is up to 8.3 milliseconds (ms), i.e. During all recording, the internal clocks of all sensors are synchronized to the GPS clock. For this we devised an automatic procedure that allows for synchroniza- tion to GPS for fast dataset generation. ![]() The correct synchronization amongst all data streams is of utmost importance. ![]()
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