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.

This week in autonomous vehicle news highlights significant regulatory developments shaping the industry's future. Federal investigators are probing crashes involving autonomous vehicle startup Avride in Texas, underscoring heightened scrutiny of safety performance and deployment practices as AV operations expand beyond controlled pilot programs. Meanwhile, Washington DC's Metro system is exploring full automation of its Red Line trains, potentially representing one of the first large-scale driverless heavy-rail transit deployments in the United States. Transit officials suggest automation could enhance train frequency, operational consistency, and reliability while reducing costs. Additionally, Alaska is advancing autonomous vehicle legislation. These developments collectively demonstrate how federal oversight and regulatory frameworks are increasingly shaping operational standards across passenger and delivery-focused autonomous systems as the technology matures.

California's Department of Motor Vehicles finalized comprehensive autonomous vehicle regulations on April 28, 2026, establishing the state's first pathway for testing and deploying heavy-duty autonomous vehicles exceeding 10,000 pounds. The regulations introduce rigorous permitting, safety-case, reporting, and enforcement requirements for all AV manufacturers while maintaining separate requirements for robotaxi operators. Though heavy-duty passenger transport AVs remain prohibited, the rules represent a significant milestone for AV trucking companies seeking access to California's substantial market. The framework provides industry clarity through detailed compliance requirements and tiered permitting for testing and commercial deployment. However, legal challenges from the California Teamsters and opposition from state and federal policymakers threaten implementation, potentially influencing AV regulatory approaches nationwide.

While they may not represent the revolution many predicted, autonomous taxi fleets are rolling out in more cities and becoming a promising urban mobility option. A BCG analysis estimates that the global robotaxi fleet could range between 700,000 to 3 million vehicles by 2035 and that fares in some markets will be lower than traditional ride-hailing services.The speed of deployment will be influenced by the costs of entering new markets, the time needed to scale up, consumers’ willingness to embrace robotaxis, and the degree to which they capture share from other transportation modes. To win in this increasingly competitive market, operators will need patience and years of substantial investment. They’ll also need to navigate complex regulatory, operational, and technological challenges.

Robotaxi networks are experiencing significant expansion into additional cities as commercial operations advance across the United States. This growth reflects increasing confidence in autonomous vehicle technology and its market viability. Companies are scaling their fleet operations to serve broader geographic regions, marking a pivotal moment in the transportation industry's evolution. As robotaxi services become more accessible, they promise to reshape urban mobility, reduce transportation costs, and address congestion challenges. The expansion demonstrates that autonomous vehicle technology has progressed from experimental phases into practical, revenue-generating operations that attract substantial investment and consumer interest.

Stellantis has announced a strategic five-year collaboration with Microsoft to accelerate its digital transformation and AI-driven innovation. The partnership will focus on co-developing over 100 AI initiatives spanning sales, customer care, and operations across the automotive company's global ecosystem. Key initiatives include strengthening cybersecurity defenses with AI-powered analytics to protect vehicles and operations, and modernizing cloud infrastructure to reduce datacenter footprint by 60% by 2029. By combining Stellantis' automotive engineering expertise and multi-brand scale with Microsoft's cloud and AI capabilities, the collaboration aims to enhance customer experiences and drive operational agility across the organization.

Software-Defined Vehicles represent a fundamental paradigm shift in automotive design, transitioning from hardware-centric to software-centric platforms capable of dynamic adaptation and continuous evolution. This comprehensive survey examines SDV architectures, enabling technologies, and operational frameworks, tracing the evolution from distributed electronic control units to centralized computing platforms. The paper reviews critical technologies including service-oriented software architectures, middleware, artificial intelligence, and cloud-based infrastructures, while introducing a structured taxonomy organizing SDV technologies across functional hardware, electrical-electronic architectures, software frameworks, automation mechanisms, and distributed infrastructure. Additionally, the study explores the Software-Defined Internet of Vehicles paradigm, integrating software-defined networking with edge and fog computing to enable scalable vehicular communication, while addressing cybersecurity and interoperability challenges inherent to modern connected vehicles.

The International Energy Agency examines the symbiotic relationship between artificial intelligence and electric vehicle development. AI and advanced computing capabilities are disproportionately benefiting EVs, particularly in autonomous driving and integrated vehicle control systems. The stable, high-voltage power supply inherent to EV batteries creates an ideal environment for sensor and chip integration. While AI benefits extend beyond EVs to hybrid vehicles and general automotive design optimization, autonomous vehicle advancement fundamentally depends on machine learning algorithms and computational power. Modern autonomous vehicles employ diverse sensor arrays including cameras, radars, and lidars to create comprehensive environmental awareness, leveraging sophisticated AI techniques and neural networks that can navigate real-world driving complexity far beyond the limitations of earlier rule-based systems confined to test tracks.