Incremental Learning for Motion Prediction of Pedestrians and Vehicles

Roboticsis undergoingamajortransformationinscopeanddimension.From a largelydominantindustrialfocus,roboticsis rapidly expandinginto human environments and vigorouslyengaged in its new challenges. Interacting with, assisting, serving, and exploring with humans, the emerging robots will - creasingly touch people and their lives. Beyond its impact on physical robots, the body of knowledge robotics has produced is revealing a much wider range of applications reaching across - verse research areas and scienti?c disciplines, such as: biomechanics, haptics, neurosciences, virtual simulation, animation, surgery, and sensor networks among others. In return, the challenges of the new emerging areas are pr- ing an abundant source of stimulation and insights for the ?eld of robotics. It is indeed at the intersection of disciplines that the most striking advances happen. TheSpringerTractsinAdvancedRobotics(STAR)isdevotedtobringingto the research community the latest advances in the robotics ?eld on the basis of their signi?cance and quality.Through a wide and timely dissemination of critical research developments in robotics, our objective with this series is to promotemoreexchangesandcollaborationsamongtheresearchersinthec- munity and contributeto further advancements inthis rapidlygrowing?eld. The monographwritten byAlejandro DizanVasquez Goveafocusesonthe practicalproblem of moving in a cluttered environment with pedestrians and vehicles. A frameworkbased on Hidden Markov models is developed to learn typical motion patterns which can be used to predict motion on the basis of sensor data. All the theoretical results have been implemented and validated with experiments, using both real and simulated data.