Two new self-driving car services--one in Atlanta from Lyft and May Mobility, another in Las Vegas from Amazon subsidiary Zoox--prove that the robotaxi race is still on. Now comes the hard part. Today, two robotaxi firms operating on opposite sides of the US say they're opening their services to the public. The Ann Arbor tech developer May Mobility has launched its self-driving car service on the Lyft app in a section of Atlanta, Georgia. Starting today, anyone who orders a Lyft in the area might be paired with an autonomous vehicle.

Even experienced drivers can be forgiven for missing a roundabout exit once or twice, but a disoriented robotaxi in Arizona did it 36 times… in a row. While Waymo taxis are among the most advanced autonomous vehicles on the road today, in a video posted earlier this month on X, a confused AV appears to be quite literally stuck in a loop. Sorry I'm late, my WAYMO did 37 laps in the roundabout pic.twitter.com/GSR4sqChV2 And yet, even with blunders like these, there were more vehicles driving themselves this year than ever before. Once cordoned off to a few test tracks and small patches of land in Mountain View, AVs are now rearing their sensors-flapping heads in more than a dozen US cities. Tens of millions of drivers, cyclists, and pedestrians are learning how to coexist amongst these machines while their shared roads serve as real-world test-beds for full-scale AV deployment.

GM's robotaxi unit Cruise has agreed to pay a 500,000 for submitting a false accident report as part of a deferred prosecution agreement. The US Justice Department (DoJ) said that Cruise failed to disclose vital details about a serious October 2023 accident in which one of its vehicles struck a pedestrian and dragged her 20 feet after she was hit by another vehicle. "Federal laws and regulations are in place to protect public safety on our roads. Companies with self-driving cars that seek to share our roads and crosswalks must be fully truthful in their reports to their regulators," said Martha Boersch, Chief of the Office of the U.S. Attorney's Criminal Division. Uber has yet to comment on the matter.

Tesla CEO Elon Musk has unveiled the company's robotaxi, Cybercab, promising it will cost less than US 30,000, and announced plans to bring autonomous driving to its Model 3 and Model Y cars in California and Texas by next year. At the much-anticipated We, Robot event hosted at Warner Bros. Studios in Burbank, California, the billionaire arrived in the Cybercab in his trademark black leather jacket, accompanied by a man dressed in a space suit. Human-like robots mingled in the crowd, danced and served drinks to those gathered for the party. In the lead-up to the announcement, analysts were sceptical that Tesla would deliver on its promise, as fully self-driving vehicles had been flagged for nine years and robotaxis for five years. Musk said there were 20 more Cybercabs at the event, in addition to the one he had arrived in, and 50 fully autonomous vehicles for attenders to try out across the 20 acres of space that Tesla had secured for the event.

Uber has entered a new deal to offer customers in select cities an option for self-driving vehicles. The partnership is with Avride, which used to be the self-driving unit for Russian conglomerate Yandex. The multi-year deal will begin by introducing Avride's self-driving robots as a delivery option for Uber Eats orders in Austin, Texas. Later this year, the robots are expected to become available for delivery orders in Dallas and Jersey City, New Jersey. Autonomous driving is slated to begin service for Uber ride requests in Dallas in 2025. It will only be an option for "qualifying orders" on either Uber or Uber Eats, but the company didn't specify what those qualifications are.

On Tuesday, Alphabet's self-driving vehicle developer Waymo said it would begin operating all-day, curbside pickups and drop-offs at Phoenix Sky Harbor International Airport in Arizona. The announcement came with little fanfare--a post on X. But it signals that after years of delay, self-driving vehicles might be (literally) moving in the right direction. The new curbside airport service sends a good signal about Waymo's business, says Mike Ramsey, an automotive analyst with Gartner. "The airport is the primary destination and departure point for any sort of mobility service, whether it's a cab, shuttle bus--or an autonomous robocab," he says.

The growing popularity of self-driving, so-called autonomous vehicles has increased the need for human-machine interfaces~(HMI) and user interaction~(UI) to enhance passenger trust and comfort. While fallback drivers significantly influence the perceived trustfulness of self-driving vehicles, fallback drivers are an expensive solution that may not even improve vehicle safety in emergency situations. Based on a comprehensive literature review, this work delves into the potential of HMI and UI in enhancing trustfulness and emotion regulation in driverless vehicles. By analyzing the impact of various HMI and UI on passenger emotions, innovative and cost-effective concepts for improving human-vehicle interaction are conceptualized. To enable a trustful, highly comfortable, and safe ride, this work concludes by discussing whether HMI and UI are suitable for calming passengers down in emergencies, leading to smarter mobility for all.

The end-to-end learning pipeline is gradually creating a paradigm shift in the ongoing development of highly autonomous vehicles, largely due to advances in deep learning, the availability of large-scale training datasets, and improvements in integrated sensor devices. However, a lack of interpretability in real-time decisions with contemporary learning methods impedes user trust and attenuates the widespread deployment and commercialization of such vehicles. Moreover, the issue is exacerbated when these cars are involved in or cause traffic accidents. Such drawback raises serious safety concerns from societal and legal perspectives. Consequently, explainability in end-to-end autonomous driving is essential to build trust in vehicular automation. However, the safety and explainability aspects of end-to-end driving have generally been investigated disjointly by researchers in today's state of the art. This survey aims to bridge the gaps between these topics and seeks to answer the following research question: When and how can explanations improve safety of end-to-end autonomous driving? In this regard, we first revisit established safety and state-of-the-art explainability techniques in end-to-end driving. Furthermore, we present three critical case studies and show the pivotal role of explanations in enhancing self-driving safety. Finally, we describe insights from empirical studies and reveal potential value, limitations, and caveats of practical explainable AI methods with respect to their safety assurance in end-to-end autonomous driving.