Japanese car giant Honda made its first annual loss in 70 years as its investments in the electric vehicle (EV) market failed to pay off. Demand for EVs has not been as strong as the company forecast, with Honda reporting a total operating loss for the year ending March 2026 of ¥423bn ($2.68bn: £1.99bn.). The firm said it was scrapping some of its EV production targets and would source parts from China, where prices are lower, to keep costs down. It cited changes in US policy as adding to its losses, including tax incentives having been taken away for US consumers purchasing EVs, and the imposition of tariffs. US consumers could previously receive up to $7,500 (£5,500) in tax credits if they purchased a new EV, but this was scrapped by President Donald Trump in September 2025.

Car manufacturer Nissan has announced it will be closing one of its UK production lines and will be cutting 900 jobs in Europe. The company confirmed it would be merging two of its lines in its Sunderland plant, but said no jobs would lost through the production change. However, the Japanese-owned car maker said it was in talks to cut about 10% of its European workforce, which included plans to close part of its warehouse in Barcelona and import cars to Nordic countries. A Nissan spokesperson said the changes were being made under its RE:Nissan recovery plan and were designed to create a leaner, more resilient business that adapts quickly to market changes. As part of this approach, today we have opened discussions with our European employees with a view to simplifying our structures, reducing complexity, and ensuring we operate in a sustainable and profitable way, they said.

Driverless cars are becoming more common in some California cities, but when the autonomous vehicles violate traffic laws, police haven't been able to ticket them - until now. The state's Department of Motor Vehicles (DMV) has announced new regulations on autonomous vehicles (AVs), including a process for police to issue a notice of AV noncompliance directly to the car's manufacturer. The new rules, which will go into effect 1 July, are part of a larger 2024 law that imposed deeper regulation on the technology. There have been a number of reports of the cars breaking traffic laws, including during a San Francisco blackout last year. The California DMV is calling the new rules the most comprehensive AV regulations in the nation.

Abstract--This survey provides an analysis of current methodologies integrating legal and logical specifications into the perception, prediction, and planning modules of automated driving systems. We systematically explore techniques ranging from logic-based frameworks to computational legal reasoning approaches, emphasizing their capability to ensure regulatory compliance and interpretability in dynamic and uncertain driving environments. A central finding is that significant challenges arise at the intersection of perceptual reliability, legal compliance, and decision-making justifiability. T o systematically analyze these challenges, we introduce a taxonomy categorizing existing approaches by their theoretical foundations, architectural implementations, and validation strategies. We particularly focus on methods that address perceptual uncertainty and incorporate explicit legal norms, facilitating decisions that are both technically robust and legally defensible. The review covers neural-symbolic integration methods for perception, logic-driven rule representation, and norm-aware prediction strategies, all contributing toward transparent and accountable autonomous vehicle operation. We highlight critical open questions and practical trade-offs that must be addressed, offering multidisci-plinary insights from engineering, logic, and law to guide future developments in legally compliant autonomous driving systems.

Hybrid traffic laws represent an innovative approach to managing mixed environments of connected autonomous vehicles (CAVs) and human-driven vehicles (HDVs) by introducing separate sets of regulations for each vehicle type. These laws are designed to leverage the unique capabilities of CAVs while ensuring both types of cars coexist effectively, ultimately aiming to enhance overall social welfare. This study uses the SUMO simulation platform to explore hybrid traffic laws in a restricted lane scenario. It evaluates static and dynamic lane access policies under varying traffic demands and CAV proportions. The policies aim to minimize average passenger delay and encourage the incorporation of autonomous vehicles with higher occupancy rates. Results demonstrate that dynamic policies significantly improve traffic flow, especially at low CAV proportions, compared to traditional dedicated bus lane strategies. These findings highlight the potential of hybrid traffic laws to enhance traffic efficiency and accelerate the transition to autonomous technology.

Autonomous vehicles in San Francisco are exempt from traffic tickets if there is nobody in the driver's seat, according to the San Francisco police department (SFPD), underscoring ongoing legal and safety concerns surrounding the expanding technology. California law has not caught up to the cars, even though they are already on the road, say public safety agencies and experts. SFPD policy states that officers can make a traffic stop of autonomous vehicles (AVs) for violations, but can only issue a citation if there is a safety driver in the vehicle overseeing its operations. Since June 2022, autonomous vehicles have been permitted to operate without safety drivers as long as they are inside the city limits. Officers can issue citations to the registered owner of an unoccupied vehicle in absentia for non-moving violations such as parking or registration offenses but not violations like speeding, running a red light, driving in the wrong lane or making an illegal turn.

Autonomous vehicles in San Francisco are exempt from traffic tickets if there is nobody in the driver's seat, according to the San Francisco police department (SFPD), underscoring ongoing legal and safety concerns surrounding the expanding technology. California law has not caught up to the cars, even though they are already on the road, say public safety agencies and experts. SFPD policy states that officers can make a traffic stop of autonomous vehicles (AVs) for violations, but can only issue a citation if there is a safety driver in the vehicle overseeing its operations. Since June 2022, autonomous vehicles have been permitted to operate without safety drivers as long as they are inside the city limits. Officers can issue citations to the registered owner of an unoccupied vehicle in absentia for non-moving violations such as parking or registration offenses but not violations like speeding, running a red light, driving in the wrong lane or making an illegal turn.

The evolution of autonomous driving has made remarkable advancements in recent years, evolving into a tangible reality. However, a human-centric large-scale adoption hinges on meeting a variety of multifaceted requirements. To ensure that the autonomous system meets the user's intent, it is essential to accurately discern and interpret user commands, especially in complex or emergency situations. To this end, we propose to leverage the reasoning capabilities of Large Language Models (LLMs) to infer system requirements from in-cabin users' commands. Through a series of experiments that include different LLM models and prompt designs, we explore the few-shot multivariate binary classification accuracy of system requirements from natural language textual commands. We confirm the general ability of LLMs to understand and reason about prompts but underline that their effectiveness is conditioned on the quality of both the LLM model and the design of appropriate sequential prompts. Code and models are public with the link \url{https://github.com/KTH-RPL/DriveCmd_LLM}.

Compliance with traffic laws is a fundamental requirement for human drivers on the road, and autonomous vehicles must adhere to traffic laws as well. However, current autonomous vehicles prioritize safety and collision avoidance primarily in their decision-making and planning, which will lead to misunderstandings and distrust from human drivers and may even result in accidents in mixed traffic flow. Therefore, ensuring the compliance of the autonomous driving decision-making system is essential for ensuring the safety of autonomous driving and promoting the widespread adoption of autonomous driving technology. To this end, the paper proposes a trigger-based layered compliance decision-making framework. This framework utilizes the decision intent at the highest level as a signal to activate an online violation monitor that identifies the type of violation committed by the vehicle. Then, a four-layer architecture for compliance decision-making is employed to generate compliantly trajectories. Using this system, autonomous vehicles can detect and correct potential violations in real-time, thereby enhancing safety and building public confidence in autonomous driving technology. Finally, the proposed method is evaluated on the DJI AD4CHE highway dataset under four typical highway scenarios: speed limit, following distance, overtaking, and lane-changing. The results indicate that the proposed method increases the vehicle's overall compliance rate from 13.85% to 84.46%, while reducing the proportion of active violations to 0%, demonstrating its effectiveness.

Self-driving vehicles have their own intelligence to drive on open roads. However, vehicle managers, e.g., government or industrial companies, still need a way to tell these self-driving vehicles what behaviors are encouraged or forbidden. Unlike human drivers, current self-driving vehicles cannot understand the traffic laws, thus rely on the programmers manually writing the corresponding principles into the driving systems. It would be less efficient and hard to adapt some temporary traffic laws, especially when the vehicles use data-driven decision-making algorithms. Besides, current self-driving vehicle systems rarely take traffic law modification into consideration. This work aims to design a road traffic law adaptive decision-making method. The decision-making algorithm is designed based on reinforcement learning, in which the traffic rules are usually implicitly coded in deep neural networks. The main idea is to supply the adaptability to traffic laws of self-driving vehicles by a law-adaptive backup policy. In this work, the natural language-based traffic laws are first translated into a logical expression by the Linear Temporal Logic method. Then, the system will try to monitor in advance whether the self-driving vehicle may break the traffic laws by designing a long-term RL action space. Finally, a sample-based planning method will re-plan the trajectory when the vehicle may break the traffic rules. The method is validated in a Beijing Winter Olympic Lane scenario and an overtaking case, built in CARLA simulator. The results show that by adopting this method, the self-driving vehicles can comply with new issued or updated traffic laws effectively. This method helps self-driving vehicles governed by digital traffic laws, which is necessary for the wide adoption of autonomous driving.