This paper focuses on safety performance testing and characterization of black-box highly automated vehicles (HAV). Existing testing approaches typically obtain the testing outcomes by deploying the HAV into a specific testing environment. Such a testing environment can involve various passively given testing strategies presented by other traffic participants such as (i) the naturalistic driving policy learned from human drivers, (ii) extracted concrete scenarios from real-world driving data, and (iii) model-based or data-driven adversarial testing methodologies focusing on forcing safety-critical events. The safety performance of HAV is further characterized by analyzing the obtained testing outcomes with a particular selected measure, such as the observed collision risk. The aforementioned testing practices suffer from the scarcity of safety-critical events, have limited operational design domain (ODD) coverage, or are biased toward long-tail unsafe cases. This paper presents a novel and informative testing strategy that differs from these existing practices. The proposal is inspired by the intuition that a relatively safer HAV driving policy would allow the traffic vehicles to exhibit a higher level of aggressiveness to achieve a certain fixed level of an overall safe outcome. One can specifically characterize such a HAV and traffic interactive strategy and use it as a safety performance indicator for the HAV. Under the proposed testing scheme, the HAV is evaluated under its full ODD with a reward function that represents a trade-off between safety and adversity in generating safety-critical events. The proposed methodology is demonstrated in simulation with various HAV designs under different operational design domains.

After more than a decade of intense focus on automated vehicles, we are still facing huge challenges for the vision of fully autonomous driving to become a reality. The same "disillusionment" is true in many other domains, in which autonomous Cyber-Physical Systems (CPS) could considerably help to overcome societal challenges and be highly beneficial to society and individuals. Taking the automotive domain, i.e. highly automated vehicles (HAV), as an example, this paper sets out to summarize the major challenges that are still to overcome for achieving safe, secure, reliable and trustworthy highly automated resp. autonomous CPS. We constrain ourselves to technical challenges, acknowledging the importance of (legal) regulations, certification, standardization, ethics, and societal acceptance, to name but a few, without delving deeper into them as this is beyond the scope of this paper. Four challenges have been identified as being the main obstacles to realizing HAV: Realization of continuous, post-deployment systems improvement, handling of uncertainties and incomplete information, verification of HAV with machine learning components, and prediction. Each of these challenges is described in detail, including sub-challenges and, where appropriate, possible approaches to overcome them. By working together in a common effort between industry and academy and focusing on these challenges, the authors hope to contribute to overcome the "disillusionment" for realizing HAV.

For nearly 50 years, FedEx's local package delivery method has largely gone unchanged, but it may soon evolve. The multinational corporation is currently working with the city of Manchester to begin testing a new last-mile delivery method. It involves a highly automated robot, resembling a mini fridge on wheels, that will transport products from local hubs to their final destinations. Thanks to state-of-the-art cameras and sensors, the FedEx Sameday Bot can efficiently cover the last leg of deliveries without a human operator. And because it can travel on sidewalks, this technology could increase shipping speed while reducing roadway congestion – greatly benefiting New Hampshirites.

Summary: With only slight tongue in cheek about the road ahead we report on the just passed House of Representative's new "Federal Automated Vehicle Policy" as well as similar policy just emerging in Germany. As a model of regulation on emerging AI technology we think they got this just about right. Just today (9/6/17) the US House of Representatives released its 116 page "Federal Automated Vehicles Policy". This still has to be reconciled and approved by the Senate but word is that shouldn't take long. Equally as interesting is that just two weeks ago the German federal government published its guidelines for Highly Automated Vehicles (HAV being the new name of choice for these vehicles).