Behavior-based Driver Identification is an emerging technology that recognizes drivers based on their unique driving behaviors, offering important applications such as vehicle theft prevention and personalized driving experiences. However, most studies fail to account for the real-world challenges of deploying Deep Learning models within vehicles. These challenges include operating under limited computational resources, adapting to new drivers, and changes in driving behavior over time. The objective of this study is to evaluate if Continual Learning (CL) is well-suited to address these challenges, as it enables models to retain previously learned knowledge while continually adapting with minimal computational overhead and resource requirements. We tested several CL techniques across three scenarios of increasing complexity based on the well-known OCSLab dataset. This work provides an important step forward in scalable driver identification solutions, demonstrating that CL approaches, such as DER, can obtain strong performance, with only an 11% reduction in accuracy compared to the static scenario. Furthermore, to enhance the performance, we propose two new methods, SmooER and SmooDER, that leverage the temporal continuity of driver identity over time to enhance classification accuracy. Our novel method, SmooDER, achieves optimal results with only a 2% reduction compared to the 11\% of the DER approach. In conclusion, this study proves the feasibility of CL approaches to address the challenges of Driver Identification in dynamic environments, making them suitable for deployment on cloud infrastructure or directly within vehicles.

For decades, my dad has been saying that he doesn't want to hear a word about self-driving cars until they exist fully and completely. Until he can go to sleep behind the wheel (if there is a wheel) in his driveway in western New York State and wake up on vacation in Florida (or wherever), what is the point? Driverless cars have long supposedly been right around the corner. Elon Musk once said that fully self-driving cars would be ready by 2019. Ford planned to do it by 2021.

Car-following (CF) modeling, a fundamental component in microscopic traffic simulation, has attracted increasing interest of researchers in the past decades. In this study, we propose an adaptable personalized car-following framework -MetaFollower, by leveraging the power of meta-learning. Specifically, we first utilize Model-Agnostic Meta-Learning (MAML) to extract common driving knowledge from various CF events. Afterward, the pre-trained model can be fine-tuned on new drivers with only a few CF trajectories to achieve personalized CF adaptation. We additionally combine Long Short-Term Memory (LSTM) and Intelligent Driver Model (IDM) to reflect temporal heterogeneity with high interpretability. Unlike conventional adaptive cruise control (ACC) systems that rely on predefined settings and constant parameters without considering heterogeneous driving characteristics, MetaFollower can accurately capture and simulate the intricate dynamics of car-following behavior while considering the unique driving styles of individual drivers. We demonstrate the versatility and adaptability of MetaFollower by showcasing its ability to adapt to new drivers with limited training data quickly. To evaluate the performance of MetaFollower, we conduct rigorous experiments comparing it with both data-driven and physics-based models. The results reveal that our proposed framework outperforms baseline models in predicting car-following behavior with higher accuracy and safety. To the best of our knowledge, this is the first car-following model aiming to achieve fast adaptation by considering both driver and temporal heterogeneity based on meta-learning.

Editor's note: Written by John Carione, the vice president of marketing at IntelliShift, a fleet and safety management company. This is one in a series of periodic guest columns by industry thought leaders. For most freight companies, the answer to the troublesome driver shortage lies in Gen Z – younger individuals who have never yet sat behind the wheel of a cab. While regulatory measures have traditionally required certain age limits in commercial vehicle operation, Congress is currently reviewing a bill that would ease these rules. The DRIVE-Safe Act, a bipartisan bill, would allow individuals under the age of 21 to cross state lines through a two-step apprenticeship program, greatly increasing the pool of potential driving candidates when it's needed the most.

IoT is already affecting most areas of our lives and transportation is no exception. Even before the age of autonomous cars IoT is improving road safety and I'm going to express this through my own personal experience and other uses. I am currently working in Dublin and I travel back home to Waterford every weekend to see my family. For the first few months, I would take the bus and with the frequent stops, distance and Dublin Traffic the journey would take 4 hours! This meant I was travelling on average for 8 hours over the weekend.

Uber is rolling out new safety features to deal with rider safety. The ride-sharing company said it is adding a direct way to call 911 from the app. File photo taken in 2017 shows ride-hailing app Uber's logo on a mobile phone in London, England. SAN FRANCISCO -- Denver law professor Nancy Leong hailed an Uber to get to the airport Tuesday morning. She says she wound up on a ride to hell, "pounding on the windows."

Nvidia recently released a new driver to fix the problems its last driver caused with Minecraft. Nvidia's GeForce Game Ready driver 378.49 that rolled out on January 24 caused Minecraft to crash as soon as it was launched, according to the Minecraft community on Reddit. To solve that problem, Nvidia just released driver 378.57, which was released as a hot fix and is only available directly from Nvidia's site. The company says it fixes crash issues in "Minecraft and some other Java-based titles," as well as resolving a problem with Pascal-based GPUs that put them into debug mode by default. The impact on you at home: Hot fix driver 378.57 is only necessary if you were running driver 378.49