HoloRadar helps give the vehicles a more complete picture of their surroundings. Breakthroughs, discoveries, and DIY tips sent six days a week. In late January, an Alphabet-owned Waymo self-driving car was cruising near an elementary school in Santa Monica, California, when a young child suddenly darted into the street . Waymo's LiDAR sensors detected the student, who had just emerged from behind a parked SUV, but it was too late. Despite slamming on the brakes and slowing from 17 to six mph, the driverless car struck the child, knocking them to the pavement.

Stuck in regulatory limbo, the self-driving-vehicle developer is encouraging residents of Washington, DC, to message public officials to help get its robotaxis onto roads. Waymo needs some help, according to an email message the self-driving developer sent to residents of Washington, DC, on Thursday. For more than a year, Waymo has been pushing city officials to pass new regulations allowing its robotaxis to operate in the district. So far, self-driving cars can test in the city with humans behind the wheel, but cannot operate in driver-free mode. The Alphabet subsidiary--and its lobbyists--have asked local lawmakers, including Mayor Muriel Bower and members of the city council, to create new rules allowing the tech to go truly driverless on its public roads.

Due to the cost and risk of deploying autonomous vehicles (A Vs) in the real world, simulation is a crucial tool in the research and development of autonomous driving software.

The company's robotaxi service is supposed to launch in the US capital this year. But while service rollouts have been relatively smooth in other cities, DC's rules have made things tricky. Waymo, the Alphabet subsidiary that develops self-driving vehicle tech, has picked up speed. The company now operates robotaxis in six cities and has announced plans to launch in a dozen others this year. It j ust raised $16 billion in a new round of funding and says it has served over 20 million rides since the company launched its service in 2020, 14 million of them in 2025 alone.

A technician works at an Amazon Web Services AI datacenter in New Carlisle, Indiana, on 2 October 2025. A technician works at an Amazon Web Services AI datacenter in New Carlisle, Indiana, on 2 October 2025. At Davos, tech CEOs laid out their vision for AI's world domination Tech chiefs waxed poetic about AI to delegates at Davos. Plus, the'human' drama of AI startups and why Tesla is thriving in Texas This week's edition is a team effort: my colleague Heather Stewart reports on the plans for AI's world domination at Davos; I examine how huge investments have followed AI companies with little to their names but drama and dreams; and Nick Robins-Early spotlights how lax regulation of autonomous driving in Texas allowed Tesla to thrive. When they weren't discussing Donald Trump, delegates at the World Economic Forum last week were being dazzled by the prospects for artificial intelligence.

Booth is a reporter at TIME. Booth is a reporter at TIME. With the widespread adoption of artificial intelligence around the world over the past year, the technology's potential to cause harm has become clearer. Reports of AI-related incidents rose 50% year-over-year from 2022 to 2024, and in the 10 months to October 2025, incidents had already surpassed the 2024 total, according to the AI Incident Database, a crowd-sourced repository of media reports on AI mishaps. Incidents arising from use of the technology, such as deepfake-enabled scams and chatbot-induced delusions have been rising steadily, according to the latest data.

Chinese robotaxis could be set to hit UK roads in 2026 as ride-sharing apps Uber and Lyft announce partnerships with Baidu to trial the tech. The two companies are hoping to obtain approval from regulators to test the autonomous vehicles in London. Baidu's Apollo Go driverless taxi service already operates in dozens of cities, mostly in China, and has accrued millions of rides without a human behind the wheel. Transport secretary Heidi Alexander said the news was another vote of confidence in our plans for self-driving vehicles - but many remain sceptical about their safety. We're planning for self-driving cars to carry passengers for the first time from spring, under our pilot scheme - harnessing this technology safely and responsibly to transform travel, Ms Alexander said in a post on X .

With the adoption of autonomous vehicles on our roads, we will witness a mixed-autonomy environment where autonomous and human-driven vehicles must learn to coexist by sharing the same road infrastructure. T o attain socially-desirable behaviors, autonomous vehicles must be instructed to consider the utility of other vehicles around them in their decision-making process. Particularly, we study the maneuver planning problem for autonomous vehicles and investigate how a decentralized reward structure can induce altruism in their behavior and incentivize them to account for the interest of other autonomous and human-driven vehicles. This is a challenging problem due to the ambiguity of a human driver's willingness to cooperate with an autonomous vehicle. Thus, in contrast with the existing works which rely on behavior models of human drivers, we take an end-to-end approach and let the autonomous agents to implicitly learn the decision-making process of human drivers only from experience. W e introduce a multi-agent variant of the synchronous Advantage Actor-Critic (A2C) algorithm and train agents that coordinate with each other and can affect the behavior of human drivers to improve traffic flow and safety.Accepted to 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021) W orkshop on Autonomous Driving: Perception, Prediction and Planning

HE development of autonomous vehicles (A Vs) is on the verge of passing beyond the laboratory and simulation tests and is shifting towards addressing the challenges that limit their practicality in today's society. While there is still need for further technological improvements to enable safe and smooth operation of a single A V, a great deal of research attention is being focused on the emerging challenge of operating multiple A Vs and the co-existence of A Vs and human-driven vehicles (HVs) [1], [2]. A realistic outlook for the adoption of autonomous vehicles on the roads is a mixed-traffic scenario in which human drivers with different driving styles and social preferences share the road with A Vs that are perhaps built by different manufacturers and hence follow different policies [3], [4]. In this work, we seek a solution that can ensure the safety and robustness of A Vs in the presence of human drivers with heterogeneous behavioral traits. Connected & autonomous vehicles (CA Vs) via vehicle-to-vehicle (V2V) communication allow vehicles to directly communicate with their neighbors, creating an extended perception that enables explicit coordination among vehicles to overcome the limitations of an isolated agent [5]-[11]. While planning in a fully A V scenario is relatively easy to achieve, coordination in the presence of HVs is a significantly more challenging task, as the A Vs not only need to react to road objects but also need to consider the behaviors of HVs [3], [4], [12]. We start by identifying the major challenges in the domain of behavior planning and prediction for A Vs in mixed-autonomy traffic.

Ensuring the safety of autonomous vehicles (AV) requires rigorous testing under both everyday driving and rare, safety-critical conditions. A key challenge lies in simulating environment agents, including background vehicles (BVs) and vulnerable road users (VRUs), that behave realistically in nominal traffic while also exhibiting risk-prone behaviors consistent with real-world accidents. We introduce Controllable Risk Agent Generation (CRAG), a framework designed to unify the modeling of dominant nominal behaviors and rare safety-critical behaviors. CRAG constructs a structured latent space that disentangles normal and risk-related behaviors, enabling efficient use of limited crash data. By combining risk-aware latent representations with optimization-based mode-transition mechanisms, the framework allows agents to shift smoothly and plausibly from safe to risk states over extended horizons, while maintaining high fidelity in both regimes. Extensive experiments show that CRAG improves diversity compared to existing baselines, while also enabling controllable generation of risk scenarios for targeted and efficient evaluation of AV robustness.