Driver Of A Vehicle Balanced News

Hummer EV's obstacle-avoiding Extract Mode adds six inches of ride height

GMC Hummer EV owners will soon be able to raise their 9,000-pound vehicle nearly six additional inches in the air, all due to a simple software update. Over the new few weeks, GMC will be adding a new “Extract Mode” — which lets drivers elevate their vehicle to avert off-road obstacles — to all First Edition models of the all-electric pickup truck. Essentially, Extract Mode lets drivers elevate their vehicle so they can avert off-road obstacles.As fun as the feature sounds, drivers should take heed that the suspension mode can only be used at low speeds. Also, frequent use of the feature will result in your vehicle needing to cool off before being suspended again. In total, Extract Mode will allow for 15.9 inches of ground clearance for the vehicle, which will give it an edge over other off-road capable vehicles like the Rivian R1T, Ram TRX, F150 Raptor and Bronco Raptor. For a preview of Extract Mode in action, check out the video below.Unfortunately, only a handful of GMC Hummer EV owners will be able to actually try out the feature. As Electreknotes, demand for the Hummer has far exceeded GMC’s expectations and the automaker is struggling to ramp up production. Only one Hummer ...
— (Engadget) (June 27)   (Pocket)

Predictive Modeling of Charge Levels for Battery Electric Vehicles using CNN EfficientNet and IGTD Algorithm. (arXiv:2206.03612v1 [cs.CV])

Convolutional Neural Networks (CNN) have been a good solution for understanding a vast image dataset. As the increased number of battery-equipped electric vehicles is flourishing globally, there has been much research on understanding which charge levels electric vehicle drivers would choose to charge their vehicles to get to their destination without any prevention. We implemented deep learning approaches to analyze the tabular datasets to understand their state of charge and which charge levels they would choose. In addition, we implemented the Image Generator for Tabular Dataset algorithm to utilize tabular datasets as image datasets to train convolutional neural networks. Also, we integrated other CNN architecture such as EfficientNet to prove that CNN is a great learner for reading information from images that were converted from the tabular dataset, and able to predict charge levels for battery-equipped electric vehicles. We also evaluated several optimization methods to enhance the learning rate of the models and examined further analysis on improving the model architecture.
— (arXiv) (June 9)   (Pocket)