Abstract: The disclosed systems and techniques facilitate efficient prediction of crowd behavior and safe and courteous navigation of crowded areas in driving environments. An example disclosed system includes a sensing system and a data processing system of a vehicle. The sensing system obtains sensing data associated with a driving environment of the vehicle. The data processing system detects, based on the sensing data, presence of vulnerable road users (VRUs) in the driving environment. The data processing system applies one or more clustering metrics to form cluster(s) of VRUs, each cluster associated with a geometric shape enclosing one or more VRUs and a velocity associated with collective motion of these VRUs. The data processing system predicts, using the geometric shapes and the associated velocities, one or more VRU-blocked regions and determine a driving path of the vehicle in the driving environment.

Abstract: An autonomous vehicle is operated along a route according to a nominal driving solution that takes into account one or more first constraints including a first predicted trajectory for an agent vehicle. An alternate scenario is determined based on one or more second external constraints that include a second predicted trajectory for the agent vehicle different from the first predicted trajectory. A risk factor on the nominal driving solution is determined for the alternative scenario, and a secondary driving solution is determined based on the risk factor and the one or more second external constraints. A candidate switching point is identified where the secondary driving solution diverges from the nominal driving solution, and the nominal driving solution is revised up to the candidate switching point based on the secondary driving solution. The autonomous vehicle is then operated based on the revised nominal driving solution.

Abstract: An autonomous vehicle is operated along a route according to a nominal driving solution that takes into account one or more first constraints including a first predicted trajectory for an agent vehicle. An alternate scenario is determined based on one or more second external constraints that include a second predicted trajectory for the agent vehicle different from the first predicted trajectory. A risk factor on the nominal driving solution is determined for the alternative scenario, and a secondary driving solution is determined based on the risk factor and the one or more second external constraints. A candidate switching point is identified where the secondary driving solution diverges from the nominal driving solution, and the nominal driving solution is revised up to the candidate switching point based on the secondary driving solution. The autonomous vehicle is then operated based on the revised nominal driving solution.

Abstract: An autonomous vehicle is operated along a route according to a nominal driving solution that takes into account one or more first constraints including a first predicted trajectory for an agent vehicle. An alternate scenario is determined based on one or more second external constraints that include a second predicted trajectory for the agent vehicle different from the first predicted trajectory. A risk factor on the nominal driving solution is determined for the alternative scenario, and a secondary driving solution is determined based on the risk factor and the one or more second external constraints. A candidate switching point is identified where the secondary driving solution diverges from the nominal driving solution, and the nominal driving solution is revised up to the candidate switching point based on the secondary driving solution. The autonomous vehicle is then operated based on the revised nominal driving solution.

Abstract: A method, apparatus and computer program product are provided for collecting activity data with a removably attached mobile device. The mobile device may be moved to various holders configured to be used in different activities. The data is collected according to a determined activity. The start and end of a session is automatically detected based on connectivity to the holder. Metrics may be tracked for specific gear, as well as various spans of time such as sessions, seasons, and/or lifetimes. Milestones and highlights may be determined for various combinations of the various activities, gear and/or terms.