The robotaxi is recording me sitting in the backseat, and I am recording it. Someone in the neighboring car is recording us both. It's an unusually hot day in San Francisco, and I am in a self-driving car named Charcuterie, operated by Cruise. Next to me is William Riggs, a professor at the University of San Francisco who studies self-driving cars. The front seats are both empty, and the wheel silently shifts as the car maneuvers itself along a thoroughfare next to Golden Gate Park.

On Dec. 29, 2019, a Honda Civic pulled up to the intersection of Artesia Boulevard and Vermont Avenue in Gardena. It was just after midnight. The traffic light was green. As the car proceeded through the intersection, a 2016 Tesla Model S on Autopilot exited a freeway, ran through a red light and crashed into the Civic. The Civic's driver, Gilberto Alcazar Lopez, and his passenger, Maria Guadalupe Nieves-Lopez, were killed instantly.

Cars are becoming increasingly automated. Drivers already benefit from a wide range of advanced driver-assistance systems (ADAS), such as lane keeping, adaptive cruise control, collision warning, and blind spot warning, which are gradually becoming standard features on most vehicles. Today's automated systems are taking over an increasing amount of responsibility for the driving task. It is expected that soon, sensors will take the place of human impulse, and artificial intelligence will substitute for human intelligence. This process is defined through various level steps, from low levels of automation where the driver retains overall control of the vehicle in level 1, to a fully-autonomous system in level 5.

Raw video: Cameras mounted inside the car catches the fatal moment. Authorites are investigating the cause of the crash. The self-driving Uber SUV that struck and killed Elaine Herzberg in Tempe, Ariz., in March picked her up on its sensors six seconds before it hit her, but did not determine that it needed to stop or evade her until it was too late, according to federal investigators. Herzberg was jaywalking her bicycle across a four-lane section of road on the night of March 18 when the Volvo XC90 SUV ran into her. A preliminary report on the accident from the National Transportation Safety Board issued on Thursday said that a review of the data from the car shows that it first identified her as an unknown object, then as a vehicle and finally as a bicycle.

Over the last few years, the quest to build fully autonomous vehicles has shifted into high gear. Yet, despite huge advances in both the sensors and artificial intelligence (AI) required to operate these cars, one thing has so far proved elusive: developing algorithms that can accurately and consistently identify objects, movements, and road conditions. As Mathew Monfort, a postdoctoral associate and researcher at the Massachusetts Institute of Technology (MIT) puts it: "An autonomous vehicle must actually function in the real world. However, it's extremely difficult and expensive to drive actual cars around to collect all the data necessary to make the technology completely reliable and safe." All of this is leading researchers down a different path: the use of game simulations and machine learning to build better algorithms and smarter vehicles.

Otto developed technology to allow big-rig trucks to drive themselves. Uber, another transportation company working on self-driving technology, acquired Otto in August. Otto developed technology to allow big-rig trucks to drive themselves. Uber, another transportation company working on self-driving technology, acquired Otto in August. Self-driving cars have been getting a lot of attention lately: Uber's self-driving taxis in Pittsburgh, Tesla's semi-autonomous Model S and the driverless Google rides that look like a cross between a Cozy Coupe and a golf cart.

BMW, Ford, and Uber have all recently said they plan to have "fully autonomous" cars ready to drive themselves on the road in 2021 (see "2021 May Be the Year of the Fully Autonomous Car"). Ford says its fleet of vehicles will lack steering wheels and offer a robotic taxi service. But don't expect to toss out your driver's license in 2021. Five years isn't long enough to create vehicles good enough at driving to roam extensively without human input, say researchers working on autonomous cars. They predict that Ford and others will meet their targets by creating small fleets of vehicles limited to small, controlled areas.

Tesla Motors's statement last week disclosing the first fatal crash involving its Autopilot automated driving feature opened not with condolences but with statistics. Autopilot's first fatality came after the system had driven people over 130 million miles, the company said, more than the 94 million miles on average between fatalities on U.S. roads as a whole. Soon after, Tesla's CEO and cofounder Elon Musk threw out more figures intended to prove Autopilot's worth in a tetchy e-mail to Fortune (first disclosed yesterday). "If anyone bothered to do the math (obviously, you did not) they would realize that of the over 1M auto deaths per year worldwide, approximately half a million people would have been saved if the Tesla autopilot was universally available," he wrote. Tesla and Musk's message is clear: the data proves Autopilot is much safer than human drivers.

Multi-agent Pathfinding is a relevant problem in a wide range of domains, for example in robotics and video games research. Formally, the problem considers a graph consisting of vertices and edges, and a set of agents occupying vertices. An agent can only move to an unoccupied, neighbouring vertex, and the problem of finding the minimal sequence of moves to transfer each agent from its start location to its destination is an NP-hard problem. We present Push and Rotate, a new algorithm that is complete for Multi-agent Pathfinding problems in which there are at least two empty vertices. Push and Rotate first divides the graph into subgraphs within which it is possible for agents to reach any position of the subgraph, and then uses the simple push, swap, and rotate operations to find a solution; a post-processing algorithm is also presented that eliminates redundant moves. Push and Rotate can be seen as extending Luna and Bekris's Push and Swap algorithm, which we showed to be incomplete in a previous publication. In our experiments we compare our approach with the Push and Swap, MAPP, and Bibox algorithms. The latter algorithm is restricted to a smaller class of instances as it requires biconnected graphs, but can nevertheless be considered state of the art due to its strong performance. Our experiments show that Push and Swap suffers from incompleteness, MAPP is generally not competitive with Push and Rotate, and Bibox is better than Push and Rotate on randomly generated biconnected instances, while Push and Rotate performs better on grids.

Optimal solutions for multi-agent pathfinding problems are often too expensive to compute. For this reason, suboptimal approaches have been widely studied in the literature. Specifically, in recent years a number of efficient suboptimal algorithms that are complete for certain subclasses have been proposed at highly-rated robotics and AI conferences. However, it turns out that the problem of non-optimal multi-agent pathfinding has already been completely solved in another research community in the 1980s. In this paper, we would like to bring this earlier related work to the attention of the robotics and AI communities.