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.

NVIDIA Alpamayo, a COMPUTEX Best Choice Award winner, represents a comprehensive open-source platform engineered to accelerate autonomous vehicle development. The system integrates vision-language-action models, simulation frameworks, reinforcement learning infrastructure, and physical AI datasets to enable Level 4 autonomy capabilities. Alpamayo empowers vehicles to perceive, reason, and respond with human-like judgment, particularly in rare and complex driving scenarios. By prioritizing reasoning-based decision-making over rigid rule-based systems, the platform advances safer, more transparent autonomous driving solutions at scale while promoting openness and accountability in the autonomous vehicle industry.

Explore how AI-native architectures are redefining ADAS competition across semiconductors, AI software, and data infrastructure landscape.

Mobileye said it will launch a direct-to-consumer robotaxi service in a major US city in 2027. It starts small, an initial fleet of roughly 100 vehicles phased ...

Pony.ai posted record Robotaxi revenue in Q1 2026, raised fleet targets to 3500 vehicles, and accelerated expansion across China, Europe, and the Middle ...

Humans have a remarkable ability to learn to drive quickly and can obtain a licence to drive across a whole country after tens of hours of practice. But after 10 years of commercial self-driving car development, over 10 million autonomous miles and $5B per year spent, we still do not have commercial self-driving vehicles on our roads. To turn what is currently a fantasy into a reality, we need to take a different approach.

Autonomous driving planning requires not only accurate trajectory prediction but also coherent semantic alignment across perception, decision making, ...

Autonomous driving in Europe faces a multifaceted array of challenges, but our review indicates that these challenges are well-understood and, with concerted effort, can be addressed. The implications of these findings are significant. In the short term, the persistence of regulatory uncertainty, technical limitations, and public scepticism means that fully self-driving vehicles will likely roll out slower in Europe than the most bullish predictions once suggested. Stakeholders must navigate a delicate balance between innovation and caution, since premature deployment could erode trust if incidents occur, whereas excessive delay could cause Europe to fall behind in a transformative industry. In the long term, overcoming these barriers is crucial because the potential benefits of AVs - from dramatically reduced traffic accidents and enhanced mobility for the elderly or disabled, to more efficient logistics and reduced congestion - are substantial. Failure to resolve the impediments could mean foregone benefits in safety and economic efficiency, not to mention Europe’s competitiveness in the global automotive arena.

The company announced earlier this month a deal with Dongfeng to build Jeep and Peugeot models for the Chinese market and increase cooperation on technology research and development.Dongfeng and other Chinese carmakers meanwhile are looking to Europe and other export markets as their home market remains tough, with consumer spending slumping.Brands such as BYD, Chery, Geely, Leapmotor, Jaecoo, and XPeng were virtually unknown three years ago in Europe.

Stellantis plans to cut more than 800,000 units of manufacturing capacity in Europe while aggressively restructuring global production around efficiency and ...

The BBC visited China’s EV factories and found they are dominating the ecosystems shaping the global auto industry.

Both the 19,000 and 28,000 figures sit within a broader group-wide target of 50,000 job cuts across Germany by 2030, covering the core Volkswagen brand, Audi, Porsche and software subsidiary Cariad. The scale of the cuts reflects the depth of VW’s financial deterioration: operating profit fell 53% to €8.9bn (US$10.3bn) in 2025, net profit dropped 44% to €6.9bn and the group’s operating margin compressed to 2.8%—its worst since the Dieselgate crisis.

Significantly, manufacturers must now submit an extensive “safety case” to obtain a permit. A safety case is a document that explains the operation of the AV ...

The EU AI Act represents the European Union's first comprehensive legal framework for artificial intelligence, introducing a risk-based regulatory approach with significant implications for the automotive sector. As vehicles increasingly incorporate AI-enabled technologies—including advanced driver assistance systems, autonomous driving functions, and driver monitoring—automotive OEMs and suppliers face new compliance obligations. The Act extends beyond technology regulation to encompass safety, compliance, and governance frameworks affecting how AI-based vehicle systems are developed, validated, monitored, and documented. Classification of AI systems according to risk levels becomes a critical compliance priority, presenting both regulatory challenges and strategic opportunities for organizations capable of developing trustworthy, transparent AI solutions that meet the Act's requirements.

La Twingo sera produite en série dès ce mois-ci sur le site slovène de Novo Mesto. Elle sera disponible en mars 2026 dans ses versions les plus équipées et en juin pour sa déclinaison la moins chère, à moins de 20 000 euros. Un dérivé Nissan et une variante Dacia sous les 18 000 euros arriveront ensuite ! Au-delà du produit, cette nouvelle citadine marque une vraie révolution copernicienne pour Renault ! Car elle aura été conçue en « deux ans seulement, contre trois à quatre ans habituellement », affirme Olivier Laik, directeur du programme des petits véhicules chez Renault. Un temps de développement record, qui permet de réduire les coûts totaux de 20 à 30% par rapport à un projet conventionnel, selon les divers calculs de la firme de Boulogne-Billancourt.

The Bosch Center for Artificial Intelligence (BCAI) serves as Bosch’s center of excellence, dedicated to integrating cutting-edge AI technologies into the company's products and services to create solutions truly “Invented for life.” By leveraging Bosch's vast domain expertise and world-class AI methods, BCAI actively spearheads applied AI projects from initial concept to implementation, bridging the gap between fundamental research and real-world applications. With a strong focus on skills, agility, and openness, BCAI teams, comprising domain experts, data professionals, and software engineers from diverse international backgrounds, are at the forefront of AI research and application.

We believe intelligence that cannot act in the physical world is incomplete. The next frontier of AI is not only to understand information, but to operate safely and intuitively in the real world. It will be defined by systems that can perceive, reason, learn, and make decisions safely in dynamic physical environments. Systems that understand uncertainty, causality, motion, interaction, and consequence.That requires a different set of problems to be solved: world models that support action, representations grounded in space and physics, policies that adapt under uncertainty, and learning systems that improve through interaction with reality rather than passive observation alone.This is the challenge of embodied AI.

The autonomous vehicle market is poised for transformative growth through 2030, with projections indicating a global market value reaching $2.1 trillion. Industry experts anticipate a robust compound annual growth rate of 22-25% from 2024 to 2030, driven by technological advancements in artificial intelligence, machine learning, and sensor technology. By 2030, approximately 60% of newly sold vehicles are expected to feature Level 2 autonomy capabilities. This expansion presents substantial opportunities across automotive manufacturing, software development, insurance, and logistics sectors. Businesses should strategically position themselves by establishing partnerships with autonomous vehicle technology providers, investing in research and development, and developing complementary services in smart infrastructure and fleet management. Companies that fail to engage with this technological shift risk competitive disadvantage in an increasingly autonomous-focused automotive landscape.