A Waymo self-driving car pulls into a parking lot in Mountain View, Calif., on May 8, 2019. A Waymo self-driving car pulls into a parking lot in Mountain View, Calif., on May 8, 2019. It was a modern mystery. In a tiny neighborhood in San Francisco's Richmond District, self-driving Waymo cars have been converging at all hours of the day and night, mystifying neighbors, KPIX reported earlier this week. Most would drive to the dead-end on 15th Avenue, where they then had no choice but to turn around and leave, according to the outlet -- and neighbors have no idea why.

Driving is an intuitive task that requires skills, constant alertness and vigilance for unexpected events. The driving task also requires long concentration spans focusing on the entire task for prolonged periods, and sophisticated negotiation skills with other road users, including wild animals. These requirements are particularly important when approaching intersections, overtaking, giving way, merging, turning and while adhering to the vast body of road rules. Modern motor vehicles now include an array of smart assistive and autonomous driving systems capable of subsuming some, most, or in limited cases, all of the driving task. The UK Department of Transport's response to the Safe Use of Automated Lane Keeping System consultation proposes that these systems are tested for compliance with relevant traffic rules. Building these smart automotive systems requires software developers with highly technical software engineering skills, and now a lawyer's in-depth knowledge of traffic legislation as well. These skills are required to ensure the systems are able to safely perform their tasks while being observant of the law. This paper presents an approach for deconstructing the complicated legalese of traffic law and representing its requirements and flow. The approach (de)constructs road rules in legal terminology and specifies them in structured English logic that is expressed as Boolean logic for automation and Lawmaps for visualisation. We demonstrate an example using these tools leading to the construction and validation of a Bayesian Network model. We strongly believe these tools to be approachable by programmers and the general public, and capable of use in developing Artificial Intelligence to underpin motor vehicle smart systems, and in validation to ensure these systems are considerate of the law when making decisions.

Could driverless cars save lives? Yes, but it may take a long road to get there. "Autonomous vehicles (AVs) are never drunk or tired or inattentive," says Harvard Business School Assistant Professor Julian De Freitas. "We expect that they will make roads truly much safer." In fact, De Freitas and colleagues recently argued in the Proceedings of the National Academy of Sciences that adopting driverless vehicles on a broad scale could improve global health on a level equivalent to the introduction of penicillin or vaccines.

For autonomous vehicles to safely share the road with human drivers, autonomous vehicles must abide by specific "road rules" that human drivers have agreed to follow. "Road rules" include rules that drivers are required to follow by law -- such as the requirement that vehicles stop at red lights -- as well as more subtle social rules -- such as the implicit designation of fast lanes on the highway. In this paper, we provide empirical evidence that suggests that -- instead of hard-coding road rules into self-driving algorithms -- a scalable alternative may be to design multi-agent environments in which road rules emerge as optimal solutions to the problem of maximizing traffic flow. We analyze what ingredients in driving environments cause the emergence of these road rules and find that two crucial factors are noisy perception and agents' spatial density. We provide qualitative and quantitative evidence of the emergence of seven social driving behaviors, ranging from obeying traffic signals to following lanes, all of which emerge from training agents to drive quickly to destinations without colliding. Our results add empirical support for the social road rules that countries worldwide have agreed on for safe, efficient driving.

If you're driving in San Francisco one week and then New York City the next, you're probably not paying attention to the small differences in rules when it comes to sharing bikes lanes, passing school buses, and turning right on red. If you're in a self-driving car, those state-by-state distinctions aren't just a nuisance (and potential ticket since ignorance isn't a legal defense), but rules the self-driving companies don't want to overlook, no matter how tedious. The software controlling the car needs to have those slight variations in traffic law programmed in, especially since companies don't want negative media attention or a blemished record showing it broke the law. Self-driving technology company Aurora looked into regulations for autonomous vehicles (known as AVs) across 29 states and found that they can vary. California has one set of testing rules, while other AV-friendly states like Texas and Arizona have others.

Traffic police in China are to begin using facial-recognition technology to identify jaywalkers and automatically issue them fines by text. Authorities in Shenzhen already publicly name and shame people who flout the southern city's strict road rules, using CCTV cameras equipped with artificial intelligence (AI) that can recognise offenders. Their faces are then displayed on large screens at crossings and on a government website. Now, the company which provides the technology is in talks with mobile phone carriers and social media firms about developing a system that notifies jaywalkers through instant messages when they are caught by the cameras, crossing the road outside of a marked pedestrian crosswalk at an intersection. "Jaywalking has always been an issue in China and can hardly be resolved just by imposing fines or taking photos of the offenders," Wang Jun, director of marketing solutions at Shenzhen-based AI firm Intellifusion, told the South China Morning Post.