As of 2026, all-solid-state commercial batteries remain strictly confined to pilot-scale testing due to a massive 3x to 5x production cost premium and severe mechanical interface degradation. Extensive global manufacturing data confirms that semi-solid-state (solid-liquid hybrid) batteries are the only technologically viable and scalable solution ready for vehicle deployment in 2026. Transitioning existing gigafactory assembly lines to semi-solid production requires an extraordinarily low equipment retrofitting capex of just 10% to 15% (approximately $1.4M to $2.1M USD per GWh), achieving an impressive 90% compatibility with legacy lithium-ion processes, while all-solid-state alternatives require a complete factory rebuild costing up to $112M USD per GWh.

Complete guide to design and install your own DIY electrical system in your camper van conversion. Wiring diagram and tutorial inside!

Tesla is preparing to unveil its CyberCab robotaxi on August 8th, marking a critical test of CEO Elon Musk's long-promised autonomous vehicle ambitions. The company is exploring an innovative subscription service model that would consolidate lease payments, Full Self-Driving subscriptions, charging, and insurance into a single monthly fee, potentially revolutionizing the automotive industry. Such a service, enhanced by credit card rewards partnerships, would force traditional automakers to fundamentally restructure their sales models. If combined with autonomous capabilities allowing owners to generate revenue by leasing vehicles through Tesla's network, the disruption would be complete. Meanwhile, Tesla continues expanding its unsupervised robotaxi service, now operating in four U.S. cities including Miami, Austin, Dallas, and Houston, demonstrating accelerating deployment of its autonomous vehicle infrastructure.

Lithium-ion battery chemistry significantly impacts asset value and lifecycle economics. Lithium iron phosphate (LFP) and nickel manganese cobalt (NMC) batteries exhibit divergent aging patterns, pricing trajectories, and end-of-life pathways despite identical State of Health metrics. For stakeholders engaged in battery procurement, sales, or deployment, understanding these chemistry-specific differences is essential for informed decision-making. The distinction directly influences profitability across second-life applications, resale markets, and recycling operations, making chemistry assessment a critical evaluation criterion often overlooked in transactions.

The global battery revolution, termed "Batteryfication," fundamentally enables the transition from combustion to electrons across transport and industry. Rather than being driven by a single breakthrough chemistry, battery advancement is characterized by compounding evolutionary improvements in cost, energy density, safety, and manufacturing scale. Historical data demonstrates that cost curve reduction has outpaced chemistry densification as the primary driver of electrification adoption. From 2010 to 2023, energy density evolved incrementally from 160 Wh/kg to 270–280 Wh/kg, reaching a practical ceiling for conventional lithium-ion architectures. The anticipated inflection point in 2026, with densities approaching 500 Wh/kg, signals a structural transformation in battery capability. Batteries remain structurally central to the energy transition, compressing costs, unlocking EV adoption, and stabilizing grids while determining performance ceilings for electrified systems.

An expert argues that five decisions – on incoterms, semiconductor visibility, tariff monitoring, routing and contacts – can create resilient EV supply ...

Autonomous driving represents a significant advancement in the transportation industry, enhancing vehicle intelligence, optimizing traffic management, and improving user experiences. Central to these innovations is deep learning, which enables systems to handle complex data and make informed decisions. Our survey explores critical applications of deep learning in autonomous driving, such as perception and detection, localization and mapping, and decision-making and control. We investigate specialized deep learning techniques, including convolutional neural networks, recurrent neural networks, self-attention transformers, and their variants, among others. These methods are applied within various learning paradigms—supervised, unsupervised, and reinforcement learning—to suit the specific needs of autonomous driving. Our analysis evaluates the effectiveness, benefits, and limitations of these technologies, focusing on their integration with other intelligent algorithms to enhance system performance. Furthermore, we examine the architectures of autonomous systems, analyzing how knowledge and information are organized from modular, pipeline-based frameworks to comprehensive end-to-end models. By presenting an exhaustive overview of the progressing domain of autonomous driving and bridging various research areas, our survey aims to synthesize diverse research threads into a unified narrative. This effort not only aims to enhance our understanding but also pushes the boundaries of what is achievable in this interdisciplinary field.

For automotive manufacturers and suppliers, that gap is practical. The industry is moving from hardware-centric vehicle programs to software-defined, electrified and connected mobility ecosystems. AI, cloud and software-defined mobility are no longer separate conversations. Vehicles are becoming more connected, more data-intensive and more dependent on software, making cloud part of the operating architecture of automotive enterprises. That matters because for AI to create value most effectively, it must work across engineering, production, supply chain, vehicle platforms and customer experience. Cloud provides the governed execution environment that makes such work possible, connecting data, applications, controls and computing so that AI can support decisions throughout the automotive operating model.

Our autonomous mobility route includes a nearly 4-mile smart mobility loop along Technology Parkway and State Route 141, with dedicated autonomous vehicle lane ...

On May 7, 2026, EU stakeholders reached a political agreement on the Digital Omnibus package, which aims to streamline existing digital regulations and reduce compliance burdens on businesses. The Digital Omnibus on AI seeks to refine the Artificial Intelligence Act through a more risk-based and innovation-friendly approach rather than undertaking a complete overhaul. For the automotive industry, which already operates under stringent product safety and regulatory frameworks, the immediate practical impact appears limited. Vehicles and certain components already fall under the AI Act's high-risk regime through existing type approval frameworks and functional safety processes. The Omnibus represents operational fine-tuning rather than fundamental restructuring of vehicle product regulations, with integration continuing primarily through established regulatory pathways.

This Technology Brief examines accelerated trends in autonomous driving regulations and international standardization between 2025 and 2026, analyzing regulatory developments across Europe, the United States, Germany, and the United Kingdom. Europe leads in establishing formal regulatory frameworks, while the U.S. and China advance development through extensive driving data and commercial operations. Leading autonomous driving companies influence legislative development by self-certifying their systems and demonstrating compliance with international safety standards via third-party verification. The report concludes that international competitiveness depends on cyclical regulatory formation balancing data accumulation, verification against UNECE standards, and iterative regulatory refinement. Success requires adopting either implementation-first or normative-first approaches and systematically integrating operational insights into evolving regulatory frameworks.

Researchers have developed a novel approach to trajectory prediction for autonomous systems by incorporating principles of embodied cognition. Rather than relying on opaque deep learning models that cannot explain their decisions, the new framework enables machines to reason about motion by simulating physical interactions, similar to how athletes anticipate movements. Published in Nature Communications, this study demonstrates that trajectory prediction systems can achieve interpretable and reliable predictions by embedding generative internal models that understand motion through simulated bodily experience, addressing critical safety concerns in autonomous vehicles and robots.

IF GRATIFICATION IS so easy, why don’t you feel more gratified already? Because it’s gotten harder. It’s still easy to experience individual feats of gratification when you find them (or they find you). But the ordinary circumstances that once produced so much gratification have gradually receded. Unseen choices in design, business, and social life have made it harder for you to engage directly with the sensory world.

Self-driving vehicles are increasingly appearing at private properties like malls and restaurants for pickups, causing chaos due to a lack of coordinated infrastructure. This burgeoning "autonomous commerce" channel, with the last-mile delivery market projected to exceed $50 billion by 2028, demands new management solutions. Autolane is addressing this by providing a platform that acts as "air traffic control" for autonomous vehicles on private land, offering communication, coordination, and control. While the U.S. market focuses on private sector solutions, Singapore leads in public sector autonomous delivery, highlighting global efforts to integrate these technologies despite a fragmented market landscape.

Pendant longtemps, l’Europe faisait de la Chine un de ses principaux leviers de croissance. Mais, comme disait MC Solaar : les temps changent. Les constructeurs chinois gagnent des parts de marché en Europe, les équipementiers voient émerger de nouveaux concurrents redoutables, les batteries sont devenues un enjeu de souveraineté et les partenariats sino-européens se multiplient.

This governmental pressure follows mounting frustration from municipal leaders and frontline emergency workers who report that robotaxis are increasingly disrupting life-saving operations. While autonomous vehicle technology has been championed as a way to reduce human traffic errors, internal feedback from cities like San Francisco and Austin paints a drastically different picture. First responder leaders revealed to regulators that the performance of driverless fleets, particularly Waymo, appears to be actively "backsliding." Vehicles are frequently committing traffic violations, freezing up on active roads, and failing to decode basic safety markers.

Prophesee and Volkswagen are collaborating to integrate neuromorphic computing and event-based vision technology into autonomous vehicles, creating perception systems that mirror human-like cognition. Unlike traditional cameras that process every pixel continuously, brain-inspired sensors activate only when detecting meaningful visual changes, substantially reducing latency, power consumption, and computational load. This innovation enables faster detection of pedestrians, obstacles, and hazards across challenging conditions, including poor lighting and adverse weather. The partnership aims to enhance self-driving vehicle safety and scalability by enabling machine-level consistency with human-like responsiveness, advancing real-world autonomous driving performance globally.

For years, the autonomous vehicle industry was consumed by a debate: cameras versus lidar, lidar versus radar, vision-only versus sensor fusion. Few voices were louder than Elon Musk’s. In 2019, Musk famously referred to lidar as a “crutch” and a “fool’s errand”, arguing that camera-based systems alone would ultimately be sufficient for autonomous driving. At the time, the comment sparked endless debate across the automotive and technology industries. Supporters of lidar argued that precise 3D measurements were essential for safe autonomy. Vision-only advocates countered that cameras, combined with increasingly powerful AI, could provide all the information a vehicle needed to navigate the world. Years later, however, the conversation has evolved. The question is no longer whether vehicles should use lidar, radar, cameras, or some combination of sensors. Instead, the industry is increasingly focused on a more important challenge: how sensing, AI, and computers work together to help machines safely understand and interact with the physical world. The future is not about choosing a single sensor. It’s about building perception systems capable of delivering reliable, real-time understanding of complex environments. 

Self-driving technology is one of the most exciting advancements in the automotive industry. While it promises safer roads and greater convenience, the price of developing and implementing autonomous vehicles (AVs) is incredibly high. From advanced sensors to powerful computing platforms, every part of an AV comes with a hefty price tag. In this article, we will break down the costs of self-driving technology, component by component, so you can understand where the money goes and what this means for the future of transportation.

Advanced end-to-end autonomous driving systems predict other vehicles' motions and plan ego vehicle's trajectory. The world model that can foresee the outcome of the trajectory has been used to evaluate the autonomous driving system. However, existing world models predominantly emphasize the trajectory of the ego vehicle and leave other vehicles uncontrollable. This limitation hinders their ability to realistically simulate the interaction between the ego vehicle and the driving scenario. In this paper, we propose a driving World Model named EOT-WM, unifying Ego-Other vehicle Trajectories in videos for driving simulation. Specifically, it remains a challenge to match multiple trajectories in the BEV space with each vehicle in the video to control the video generation. We first project ego-other vehicle trajectories in the BEV space into the image coordinate for vehicle-trajectory match via pixel positions.