To date, China's self-driving road test efforts have lagged behind other regions. The California Vehicle Administration (DMV) says 64 companies have been granted licenses for road tests with a human in the passenger seat, with former Google self-driving project Waymo the sole company in the state permitted to test without a human in the vehicle. Waymo has completed 2.34 million km of California road tests, followed by GM Cruise's 1.33 million km and others such as Pony.ai, It's not surprising that California is a world leader in self-driving road testing, considering the large number of AI companies located in the state. But a recent report suggests China has picked up speed, with Beijing emerging as a new self-driving vehicle hot spot.

ADAS (Advanced Driving Assistance Systems) and AD (Autonomous Driving) systems are the next big frontier for automotive companies. The challenge lays in finding the right balance between minimizing the number of accidents and casualties while maximizing the comfort of traveling in complex conditions. ADAS/AD functions combine a number of components, including sensors (hardware and software processing), the algorithm fusing the data coming from multiple sensors, the algorithm deciding to act upon those inputs (braking, steering, accelerating), and finally, the actuators that will be implementing the decision. ADAS/AD functions are also divided into a number of "levels", each dictating who is responsible for the action, the car or the driver. From level-0 to level-2, the systems are the "eyes-on and hands-on" type, meaning that the function is there to support the driver in supplying more information or automating some parts of the driving. From level-3 to level-5, the vehicle is in charge and can eventually (in the case of level-3 and level-4) give the controls back to the driver if the driving condition is too complex.

Although we have focused on the "hot" area of A Vs, our discussion and the novel CBI theorems are more generally applicable. We see them as especially useful now for MLbased systems with critical applications, although not with extreme requirements, since assurance in these systems must rely on combinations of statistical evidence with other verification methods that are, as yet, not well-established. A PPENDIX A. Statement And Proof of CBI Theorem 1 Problem: Consider the set D of all probability distributions defined over the unit interval, each distribution representing a potential prior distribution of pfm values for an A V . For 0 p l null null 1, we seek a prior distribution that minimises the posterior confidence in a reliability bound p [ p l, 1], given k fatalities have occurred over n miles driven and subject to constraints on some quantiles of the prior distribution. That is, for θ (0, 1], we solve minimise D Pr ( X null p k & n) subject to Pr ( X null null) θ, Pr (X null p l) 1 Solution: There is a prior in D that minimises the posterior confidence: the 2-point distribution Pr ( X x) θ 1 x x 1 (1 θ)1 x x 3 where p l null x 1 null null x 3, and the values of x 1 and x 3 both depend on the model parameters (i.e.

The safety of self-driving cars has become a source of concern for U.S. transportation regulators this year after one of Uber Technologies Inc's [UBER.UL] vehicles struck and killed a woman in March in Arizona, prompting the company to shut down its testing efforts for a time. Uber has said it plans to have self-driving cars back on the road by the end of the year.

Apple's plans to develop and manufacture its own branded car may have fallen by the wayside, but the company still remains interested in developing self-driving technologies. So while dozens of engineers have left Project Titan in recent months, Tim Cook this past summer essentially confirmed Apple's ongoing interest in the automotive space while also noting that autonomous driving is now the primary focus of Apple's car ambitions. "We're focusing on autonomous systems," Cook said this past June. "It's a core technology that we view as very important." Cook later added that the entire initiative is "probably one of the most difficult AI projects to work on."