The goal of autonomous driving ist to develop the capacity of sensing and moving through general traffic environments with little to no auxiliary inputs. Research on autonomous driving is gaining traction with the increasing availability of realistic simulation engines and large scale datasets. Due to its recent advancements ML and especially Deep reinforcement learning becomes increasingly important also for path planning and path plan improvements and for driving decisions.
Current self-driving systems already work well in simple scenarios (e.g. highways). However, driving in complex environments is still difficult. New challenges arise when the system has to consider the potentially combined occurrence of elaborate traffic conditions, pedestrian behavior, and unusual driving situations. The self-driving vehicles of the future need to be able to work under diverse environments and a wide variety of conditions (e.g. varying weather, uncontrollable behavior of other road users, movable traffic lights, etc.). Advancing the state of the art in autonomous driving lies at the heart of the research problem that our team at JKU investigates.
recent publications in AI 4 Driving:
- SpringerVisual Scene Understanding for Autonomous Driving Using Semantic Segmentation2019
- arXivPatch Refinement - Localized 3D Object Detection2019