Recent advancements in large vision language models (VLMs) tailored for autonomous driving (AD) have shown strong scene understanding and reasoning capabilities, making them undeniable candidates for end-to-end driving systems. However, limited work exists on studying the trustworthiness of DriveVLMs -- a critical factor that directly impacts public transportation safety. In this paper, we introduce AutoTrust, a comprehensive trustworthiness benchmark for large vision-language models in autonomous driving (DriveVLMs), considering diverse perspectives -- including trustfulness, safety, robustness, privacy, and fairness. We constructed the largest visual question-answering dataset for investigating trustworthiness issues in driving scenarios, comprising over 10k unique scenes and 18k queries. We evaluated six publicly available VLMs, spanning from generalist to specialist, from open-source to commercial models. Our exhaustive evaluations have unveiled previously undiscovered vulnerabilities of DriveVLMs to trustworthiness threats. Specifically, we found that the general VLMs like LLaVA-v1.6 and GPT-4o-mini surprisingly outperform specialized models fine-tuned for driving in terms of overall trustworthiness. DriveVLMs like DriveLM-Agent are particularly vulnerable to disclosing sensitive information. Additionally, both generalist and specialist VLMs remain susceptible to adversarial attacks and struggle to ensure unbiased decision-making across diverse environments and populations. Our findings call for immediate and decisive action to address the trustworthiness of DriveVLMs -- an issue of critical importance to public safety and the welfare of all citizens relying on autonomous transportation systems. Our benchmark is publicly available at \url{https://github.com/taco-group/AutoTrust}, and the leaderboard is released at \url{https://taco-group.github.io/AutoTrust/}.

He used to go to the Regal City North cinema in Chicago three times a week. But he never goes there anymore--because of the parking lot. The parking garage, which is directly connected to the theater, once charged 2 for parking. Then it fell into disrepair sometime during the pandemic. "Someone destroyed the crossbar at the exit, and the stairwells had broken glass in them. They never replaced the glass for the stairwell," Spencer told me.

ZeroEyes CEO Mike Lahiff joins'Fox & Friends' to explain how the technology works to help keep students safe in schools. Google's flagship developer conference called I/O just wrapped up with interesting leaps in how the Big Tech giant is planning to change the world. Here are the seven biggest things we learned from Google at I/O 2024. Google's I/O event was largely an opportunity for it to make its case to developers -- and, to a lesser extent, consumers -- as to why its artificial intelligence is ahead of rivals Microsoft and OpenAI. Here's a rundown of the seven highlights to keep an eye on.

With the robust development of technology, license plate recognition technology can now be properly applied in various scenarios, such as road monitoring, tracking of stolen vehicles, detection at parking lot entrances and exits, and so on. However, the precondition for these applications to function normally is that the license plate must be 'clear' enough to be recognized by the system with the correct license plate number. If the license plate becomes blurred due to some external factors, then the accuracy of recognition will be greatly reduced. Although there are many road surveillance cameras in Taiwan, the quality of most cameras is not good, often leading to the inability to recognize license plate numbers due to low photo resolution. Therefore, this study focuses on using super-resolution technology to process blurred license plates. This study will mainly fine-tune three super-resolution models: Real-ESRGAN, A-ESRGAN, and StarSRGAN, and compare their effectiveness in enhancing the resolution of license plate photos and enabling accurate license plate recognition. By comparing different super-resolution models, it is hoped to find the most suitable model for this task, providing valuable references for future researchers.

The Papago GoSafe S810 camera duo has more "safety" features than you can shake a stick at, including one I'd never even considered--stop sign recognition. It recognizes stop signs and pops the digital equivalent up on its display. Kind of fun, but as I'm wont to say: If you need this stuff, call a cab or wait for self-driving vehicles. Admonishment aside, the $170 S810 is more than just fancy features. It takes very, very good day and night video, and the rear camera, unlike some we've seen recently, actually captures enough detail to be useful.

This paper studies the problems of vehicle make & model classification. Some of the main challenges are reaching high classification accuracy and reducing the annotation time of the images. To address these problems, we have created a fine-grained database using online vehicle marketplaces of Turkey. A pipeline is proposed to combine an SSD (Single Shot Multibox Detector) model with a CNN (Convolutional Neural Network) model to train on the database. In the pipeline, we first detect the vehicles by following an algorithm which reduces the time for annotation. Then, we feed them into the CNN model. It is reached approximately 4% better classification accuracy result than using a conventional CNN model. Next, we propose to use the detected vehicles as ground truth bounding box (GTBB) of the images and feed them into an SSD model in another pipeline. At this stage, it is reached reasonable classification accuracy result without using perfectly shaped GTBB. Lastly, an application is implemented in a use case by using our proposed pipelines. It detects the unauthorized vehicles by comparing their license plate numbers and make & models. It is assumed that license plates are readable.