Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set? If the dataset is a sample, then what is the larger set? Is the sample representative of the larger set (e.g., geographic coverage)? If so, please describe how this representativeness was validated/verified. If it is not representative of the larger set, please describe why not (e.g., to cover a more diverse range of instances,

Sen. Josh Hawley, R-Mo., commends President Donald Trump tearing into America's nation builders in the Middle East and weighs in on a Wisconsin judge being indicted for hiding an illegal immigrant from ICE on'The Ingraham Angle.' FIRST ON FOX – Sen. Josh Hawley, R-Mo., asked the Justice Department on Thursday to investigate a Chinese-owned self-driving trucking company, one of the largest in the U.S., citing allegations that it had shared proprietary data and other sensitive technology with state-linked entities in Beijing. The letter, sent to U.S. Attorney General Pam Bondi and previewed exclusively to Fox News Digital, asks the Justice Department to open a formal investigation into the autonomous truck company TuSimple Holdings, a Chinese-owned company and one of the largest self-driving truck companies in the U.S. In it, Hawley cites recent reporting from the Wall Street Journal that alleges that TuSimple "systematically shared proprietary data, source code, and autonomous driving technologies" with Chinese state-linked entities-- what he described as "blatant disregard" of the 2022 national security agreement with the Committee on Foreign Investment in the United States, or CFIUS. "These reports also revealed communications from TuSimple personnel inside China requesting the shipment of sensitive Nvidia AI chips and detailed records showing'deep and longstanding ties' with Chinese military-affiliated manufacturers," Hawley said. Sen. Josh Hawley, R-Mo., wants the Justice Department to investigate TuSimple Holdings, a Chinese-owned self-driving trucking company. He noted that to date, TuSimple "has not faced serious consequences" for sharing American intellectual property with China, despite having continued to share data with China after signing a national security agreement with the U.S. government in 2022, which was enforced by the Committee on Foreign Investment in the U.S. "If the reports about TuSimple are accurate, they represent not just a violation of export law, but a breach of national trust and a direct threat to American technological leadership," Hawley said.

Lane detection is a vital task for vehicles to navigate and localize their position on the road. To ensure reliable results, lane detection algorithms must have robust generalization performance in various road environments. However, despite the significant performance improvement of deep learning-based lane detection algorithms, their generalization performance in response to changes in road environments still falls short of expectations. In this paper, we present a novel framework for single-source domain generalization (SSDG) in lane detection. By decomposing data into lane structures and surroundings, we enhance diversity using High-Definition (HD) maps and generative models. Rather than expanding data volume, we strategically select a core subset of data, maximizing diversity and optimizing performance. Our extensive experiments demonstrate that our framework enhances the generalization performance of lane detection, comparable to the domain adaptation-based method.

Unsupervised Domain Adaptation demonstrates great potential to mitigate domain shifts by transferring models from labeled source domains to unlabeled target domains. While Unsupervised Domain Adaptation has been applied to a wide variety of complex vision tasks, only few works focus on lane detection for autonomous driving. This can be attributed to the lack of publicly available datasets. To facilitate research in these directions, we propose CARLANE, a 3-way sim-to-real domain adaptation benchmark for 2D lane detection. CARLANE encompasses the single-target datasets MoLane and TuLane and the multi-target dataset MuLane. These datasets are built from three different domains, which cover diverse scenes and contain a total of 163K unique images, 118K of which are annotated. In addition we evaluate and report systematic baselines, including our own method, which builds upon Prototypical Cross-domain Self-supervised Learning. We find that false positive and false negative rates of the evaluated domain adaptation methods are high compared to those of fully supervised baselines. This affirms the need for benchmarks such as CARLANE to further strengthen research in Unsupervised Domain Adaptation for lane detection. CARLANE, all evaluated models and the corresponding implementations are publicly available at https://carlanebenchmark.github.io.

Join TuSimple and help change the way the world moves. Come join a higher calling and find a deeper purpose! As a multi-national Artificial Intelligence Technology Company, we are at the epicenter of the Autonomous Vehicle Universe. Our breakthroughs are leading the industry in autonomous trucking. While inventing the framework of Autonomous Driving, our current fleet of autonomous Trucks are helping communities receive much-needed supplies and medical equipment around the clock.

Being available now, will HAD play a role as a steppingstone to adoption of full L4 systems? And, if HAD has strong uptake, will it motivate the truck OEMs to accelerate their own rollout of such systems? In the near term, it will be fascinating to see what transpires with Traton and their U.S. subsidiary Navistar, now that their technology partnership with TuSimple is kaput. I'm quite certain they aren't sitting on their hands; they seek to have a strong play in the AV truck market as a strategic necessity. For highway operations, current efforts aim to automate the "ramp to ramp" long haul, augmented by transferring the load to human driven trucks for the last mile.

Join TuSimple and help change the way the world moves. TuSimple's Autonomous Operations Group is searching for an Operations Supervisor to manage the first shift/morning shift/day shift and to ensure the day-to-day operations are conducted to normal operating specifications. This includes the hiring, coaching, training, monitoring performance, scheduling, payroll and managing workloads for both the Test Operations and the Fleet Operations at our Dallas Autonomous Operations location. TuSimple is a fully commercialized autonomous trucking company. As a multi-national Artificial Intelligence Technology Company, we are at the epicenter of the Autonomous Vehicle Universe.

TuSimple Holdings Inc. co-founder Mo Chen has taken control of the self-driving trucking company as federal authorities continue to investigate TuSimple's relationship with Mr. Chen's other startup, a Chinese hydrogen-trucking company. A TuSimple filing with the Securities and Exchange Commission on Wednesday shows that Mr. Chen has 59% of the voting power at the San Diego-based company, giving him control as of Nov. 9, a day before the company announced it had ousted its board of directors. Mr. Chen acquired the stake through stock purchases using his family trust and British Virgin Islands-based entities, according to the securities filing. TuSimple's newly appointed chief executive officer, Cheng Lu, said, "We have a strong sense of urgency to put our company back on track and regain trust from all stakeholders." A weekly digest of tech reviews, headlines, columns and your questions answered by WSJ's Personal Tech gurus.

We focus on bridging domain discrepancy in lane detection among different scenarios to greatly reduce extra annotation and re-training costs for autonomous driving. Critical factors hinder the performance improvement of cross-domain lane detection that conventional methods only focus on pixel-wise loss while ignoring shape and position priors of lanes. To address the issue, we propose the Multi-level Domain Adaptation (MLDA) framework, a new perspective to handle cross-domain lane detection at three complementary semantic levels of pixel, instance and category. Specifically, at pixel level, we propose to apply cross-class confidence constraints in self-training to tackle the imbalanced confidence distribution of lane and background. At instance level, we go beyond pixels to treat segmented lanes as instances and facilitate discriminative features in target domain with triplet learning, which effectively rebuilds the semantic context of lanes and contributes to alleviating the feature confusion. At category level, we propose an adaptive inter-domain embedding module to utilize the position prior of lanes during adaptation. In two challenging datasets, ie TuSimple and CULane, our approach improves lane detection performance by a large margin with gains of 8.8% on accuracy and 7.4% on F1-score respectively, compared with state-of-the-art domain adaptation algorithms.