Research from the ride-hail aggregator Obi finds Waymo is starting to edge up on Uber and Lyft in San Francisco. Tesla, which operates a ride-hail service with human drivers, is winning the price wars. In San Francisco, people wanting to get from point A to point B have a few fairly unique options. Then, starting last fall, Bay Area denizens also got access to electric automaker Tesla's ride-hail service, which operates as a "robotaxi" in Texas but as a more traditional service, with drivers behind the wheel, in California. For months, the new and futuristic "robotaxi" services felt like a novelty .

Children and parked cars are color-coded on a monitor inside a Mercedes-Benz S-Class during an autonomous driving and AI demonstration in Immendingen, Germany on July 17, 2018. Children and parked cars are color-coded on a monitor inside a Mercedes-Benz S-Class during an autonomous driving and AI demonstration in Immendingen, Germany on July 17, 2018. In 2025, misconceptions about AI flourished as people struggled to make sense of the rapid development and adoption of the technology. Here are three popular ones to leave behind in the New Year. When GPT-5 was released in May, people wondered (not for the first time) if AI was hitting a wall.

A blackout in San Francisco revealed a new way for robotaxis to go wrong. Waymo's self-driving robotaxis can successfully nail a tricky left turn, weave through lanes to drop you off at the airport, and safely pass a U-Haul that's idling in the middle of the street. But during a blackout, they apparently turn into four-wheel bricks. On Saturday, when a major power outage in San Francisco knocked out traffic signals, many Waymo vehicles didn't pull over to the side of the road or seek out a parking space. Nor did they treat intersections as four-way stops, as a human would have. Instead, they just sat there with their hazard lights on, like a student driver freezing up before their big parallel-parking test.

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

Kodiak AI's autonomous truck system achieved a safety score of 98 in Nauto's evaluation, tying with the safest human commercial fleets among over 1,000 tested.

Previous work has shown that when multiple selfish Autonomous Vehicles (AVs) are introduced to future cities and start learning optimal routing strategies using Multi-Agent Reinforcement Learning (MARL), they may destabilize traffic systems, as they would require a significant amount of time to converge to the optimal solution, equivalent to years of real-world commuting. We demonstrate that moving beyond the selfish component in the reward significantly relieves this issue. If each AV, apart from minimizing its own travel time, aims to reduce its impact on the system, this will be beneficial not only for the system-wide performance but also for each individual player in this routing game. By introducing an intrinsic reward signal based on the marginal cost matrix, we significantly reduce training time and achieve convergence more reliably. Marginal cost quantifies the impact of each individual action (route-choice) on the system (total travel time). Including it as one of the components of the reward can reduce the degree of non-stationarity by aligning agents' objectives. Notably, the proposed counterfactual formulation preserves the system's equilibria and avoids oscillations. Our experiments show that training MARL algorithms with our novel reward formulation enables the agents to converge to the optimal solution, whereas the baseline algorithms fail to do so. We show these effects in both a toy network and the real-world network of Saint-Arnoult. Our results optimistically indicate that social awareness (i.e., including marginal costs in routing decisions) improves both the system-wide and individual performance of future urban systems with AVs.

The transition of control from autonomous systems to human drivers is critical in automated driving systems, particularly due to the out-of-the-loop (OOTL) circumstances that reduce driver readiness and increase reaction times. Existing takeover strategies are based on fixed time-based transitions, which fail to account for real-time driver performance variations. This paper proposes an adaptive transition strategy that dynamically adjusts the control authority based on both the time and tracking ability of the driver trajectory. Shared control is modeled as a cooperative differential game, where control authority is modulated through time-varying objective functions instead of blending control torques directly. To ensure a more natural takeover, a driver-specific state-tracking matrix is introduced, allowing the transition to align with individual control preferences. Multiple transition strategies are evaluated using a cumulative trajectory error metric. Human-in-the-loop control scenarios of the standardized ISO lane change maneuvers demonstrate that adaptive transitions reduce trajectory deviations and driver control effort compared to conventional strategies. Experiments also confirm that continuously adjusting control authority based on real-time deviations enhances vehicle stability while reducing driver effort during takeover.