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.

Your next ride in Austin could be driverless. Ride-hailing company Lyft is now offering riders the option of choosing an autonomous vehicle in some areas of Austin. When it established a partnership with Ford and Argo last year, Lyft said that it planned to expand its autonomous vehicles to Austin this year. Ford and Argo have been operating in Austin since 2019 to test autonomous vehicle technology, deploying prototypes to establish the city as a proving ground. Driverless vehicles have been tested in Austin since at least 2015.

AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner The ultimate goal has always been to build AV fleets that are scalable, deployable, and safe. The community hopes to reach better-than-human performance on the road regardless of physical conditions or environment. Well, how safe is safe? While avoiding obstacles, following traffic laws, and looking out for pedestrians sound simple, the complexities occur when vehicles find themselves in novel situations never encountered during training, that a human could easily navigate. A common observation is AV rollouts being called back or delayed due to sudden faults or accidents.

Disengagement is a situation when the vehicle returns to manual control or the driver feels the need to take back the wheel from the AV decision system. I came across this news article a while ago about a man dozing off at the wheel after switching his Tesla to autonomous mode, and being criminally charged soon after because the vehicle was speeding unbeknownst to him. A quick search revealed several such reports on drivers being charged for unlawful practices in semi-autonomous vehicles. This got me thinking: how will traffic laws change as we slowly enter the autonomous vehicle era, and in general, the AI-driven 21st century? Most importantly, this brings up the question of whom to blame when dealing with adverse human-robot interactions. These aren't new questions – only questions to which new perspectives can continually be added until a final course of action is decided. While I actively try to avoid the philosophical and ethical underpinnings of the matter, I will cover the current progress in autonomous vehicle technology, trends and limitations of today's autonomous vehicle policy, and possible directions to better facilitate the transition to autonomous vehicles around the globe. The last decade or so has been a very exciting time in the self-driving vehicle space.