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Going live for FOMO HOUR in 10 minutes!Farokh is back and we are talking:-$110k Bitcoin and the SZN that shall not be named-OpenAI vs Robinhood-Memes catching a bid+ 4 Yeet spins + a lot moreJoin us live on Kick or spaces at 10 am ET!Farokh (Perma/Bull) (@farokh):Looks good, send it!Back at the helm of FOMO HOUR, Monday to Friday live on KICK!https://t.co/aL7PR1ItDC ...

点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近15个 方向 学习 路线 随着神经场景表征的发展，之前出现了一些方法尝试用神经辐射场重建街道场景，像Block-NeRF 。但是它无法处理街道上的动态车辆，而这是自动驾驶环境仿真 中的关键要素。最近一些方法提出将动态驾驶场景表示为由前景移动汽车和静态背景组成的组合神经表示。为了处理动态移动的目标车辆，这些方法利用跟踪的 车辆姿态来建立观察空间和规范空间之间的映射，在那里他们使用 NeRF 网络来模拟汽车的几何形状和外观。虽然这些方法产生了合理的结果，但它们仍然局限 于高训练成本和低渲染速度。基于这些前述工作，浙大提出了S treet Gaussians。笔者有幸参与了公司新一代闭环仿真系统的开发，花了几个月的时间，终于把基 于Street Gaussians的算法落地。今天就分享下自己的一些看法~ 下图是在Waymo数据集上的渲染结果。street gaussians的方法在训练半小时内以 135 FPS的速度产生高质量的分辨率为1066×1600渲染视角。这两个基于NeRF的方 法存在训练和渲染成本高的问题。 以前的方法通常面临训练 ...

Core Viewpoint - Innoviz Technologies Ltd. has regained compliance with Nasdaq's minimum bid price requirement, confirming its closing bid price was at least $1.00 for 10 consecutive business days prior to the September 22, 2025 deadline [1][2][3] Company Summary - Innoviz is a leading Tier-1 supplier of high-performance, automotive-grade LiDAR sensors and perception software, aiming to enhance safety in autonomous vehicles [1][4] - The company operates globally, serving major automotive manufacturers and has been selected by premium car brands for consumer vehicles and various commercial applications [4]

Tesla could really use the affordable EV it's been promising, but the timeline keeps slipping. As recently as April, Tesla said it would begin production of its mysterious new "affordable" EVs in the first half of 2025. But the deadline came and went on Tuesday with no word, and Wednesday saw the automaker report its second consecutive year-over-year decline in deliveries.When Tesla announced similarly bad delivery numbers in April, an analyst told Business Insider it made making an affordable EV even more ...

Core Insights - The autonomous driving sector is seen as a significant opportunity within the AI landscape, with various investment avenues available [1] - Waymo has established a first-mover advantage in the self-driving taxi market, completing over a quarter of a million paid rides weekly, a fivefold increase from the previous year [4] - Tesla has recently launched its robotaxi service, raising questions about how it will compete with Waymo's early success [5] Company Approaches - Waymo and Tesla have fundamentally different strategies for developing self-driving fleets, focusing on distinct technical variables such as mapping and data collection [7] - Waymo emphasizes simulating real-world environments, while Tesla relies on a data-heavy feedback loop from actual driver behavior [8] - Tesla has gathered over 3.5 billion miles of driver data to enhance its autonomous driving software [9] Research Findings - Waymo's research suggests that collecting extensive data is crucial for scaling autonomous vehicle platforms, indicating that real driver behavior is vital for model training [10] - The findings from Waymo's study appear to support Tesla's approach of learning from actual driver behaviors and iterating technology based on collected data [11] Market Dynamics - The future success in the autonomous driving market will depend on customer acquisition, market expansion, partnerships with ride-hailing services, and the ability to scale fleets profitably [14] - Despite Waymo's current lead, the competitive landscape remains fluid, and Tesla's approach may still hold significant merit [13]

Tesla’s Full Self-Driving (FSD) will power my San Jose to Orange County trip, showcasing its advanced capabilities. More significantly, Tesla’s Robotaxi service, launched in Austin on June 22, 2025, is the first mass-scale autonomous ride-hailing platform to challenge Uber and Lyft. Starting with 10-20 Model Ys offering $4.20 rides in South Austin, it’s invite-only with safety monitors but leverages FSD’s vast data for rapid expansion, targeting cities like Los Angeles by 2026. This marks a pivotal shift in ...

🚖 **Tesla Robotaxi Shines Bright at Night!** 🌌Just had a mind-blowing ride with @DirtyTesLa, and let me tell you—Tesla’s Robotaxi is a game-changer! 🚨 Watched it flawlessly handle an ambulance zooming by, navigating like a pro in the dark.https://t.co/Zk5N8QzM9j ...

Core Insights - Technological development does not always exhibit exponential growth; after initial breakthroughs, growth tends to slow down [2][4] - As the gap in foundational models narrows, the focus of industry competition shifts from "technological leadership" to "product experience," creating opportunities for startups [2][6] - A product that fails to establish a strong data barrier or user experience moat is vulnerable to being integrated or replaced by foundational models [2][13] - AI will not change fundamental human needs but has the potential to reshape service delivery methods and service logic, leading to richer interactions and greater system extensibility [2][14] Industry Dynamics - The initial optimism surrounding technologies like ChatGPT has given way to caution as the industry faces pre-training bottlenecks, similar to past expectations in autonomous driving [4][5] - The current stage of AI development can be likened to the mobile internet's evolution, where the emergence of open-source models parallels the explosive growth of the Android platform [8][9] - Companies that enhance existing demand efficiency with new technologies are more likely to succeed than those that create demand for new technologies [9][11] - The infrastructure evolution, such as the rollout of 4G, significantly impacts the growth of applications, similar to how AI's development is currently unfolding [9][11] Competitive Landscape - Major companies are rapidly positioning themselves in key areas of the foundational model chain, which may limit opportunities for startups [10] - AI's ability to enhance business efficiency and penetrate deeply into various sectors suggests that its impact will surpass that of the mobile internet era [11][12] - The phrase "model equals application" highlights the fundamental shift in the competitive landscape, where model upgrades can quickly render certain startup projects obsolete [13][14] Service Innovation - AI's general capabilities are often insufficient for practical applications, revealing limitations that can become entry points for new innovations [14][15] - AI can fundamentally reconstruct service logic rather than merely digitizing existing processes, allowing for personalized service strategies with minimal marginal costs [15]

The future is autonomous https://t.co/JfosqqHaXP ...