Connected automated driving promises a significant improvement of traffic efficiency and safety on highways and in urban areas. Cooperative maneuver planning may facilitate active guidance of connected automated vehicles at intersections. Research in automatic intersection management put forth a large body of works that mostly employ rule-based or optimization-based approaches primarily in fully automated simulated environments. In this work, we compare two cooperative planning approaches for unsignalized intersections that are capable of handling mixed traffic, i.e., the road being shared by automated vehicles and regular vehicles driven by humans. The first approach is a cooperative planner that selects the most efficient out of multiple possible maneuvers based on a scene prediction trained on real driving data. The second cooperative planning approach is based on graph-based reinforcement learning, which conquers the lack of ground truth data for cooperative maneuvers. We thoroughly evaluate both cooperative planners in a realistic high-fidelity simulation with fully automated traffic and mixed traffic. The simulative experiments show that cooperative maneuver planning leads to less delay due to interaction and a reduced number of stops. Furthermore, we present results from real-world experiments with three prototype automated vehicles at a T-junction in public traffic, in which both planning modules demonstrate their ability to perform efficient cooperative maneuvers.

Check out all the on-demand sessions from the Intelligent Security Summit here. Classical machine learning (ML) algorithms have proven to be powerful tools for a wide range of tasks, including image and speech recognition, natural language processing (NLP) and predictive modeling. However, classical algorithms are limited by the constraints of classical computing and can struggle to process large and complex datasets or to achieve high levels of accuracy and precision. Enter quantum machine learning (QML). QML combines the power of quantum computing with the predictive capabilities of ML to overcome the limitations of classical algorithms and offer improvements in performance.

Then, suddenly, both technology and businesses took a different course. Chris Monroe, co-founder of and chief scientist at quantum computing company IonQ, offers a simple explanation for the abrupt change in interest. "Supercomputers failed to catch on because, although they bring the promise of speed and ability to process large computational problems, they come with a significant physical footprint [and] energy/cooling requirements," he notes. "When it comes to mainstream adoption, supercomputers never hit the right balance of affordability, size, access, and value-add enterprise use cases." Supercomputers have traditionally been defined by the fact that they bring together a collection of parallel hardware providing a very high computational throughput and rapid interconnections.

Deloitte has released a slew of predictions for 2021, including in the enterprise tech, data and tech, media, telecom spaces. Deloitte picked resilience as the theme for its 12th annual tech trends report; a word that became a mantra in nearly every organization after their 2020 plans were upended by the coronavirus pandemic. In a webinar Monday, the firm identified nine trends separated into three groups that focus on how organizations can use technology to digitize, modernize, and enhance their businesses. Some have been spurred by COVID-19 and some by changes that have been ongoing for years, said Scott Buchholz, a managing director with Deloitte Consulting and emerging tech research director. The first group is dubbed "Strategy, engineered," and addresses the notion that the corporate and tech strategies "have really become intertwined as we move forward and increasingly become one and the same," Buchholz said.