Intelligent Environment Enabling Autonomous Driving

Abstract

Automated driving is expected to enormously evolve the transportation industry and ecosystems. Advancement in communications and sensor technologies have further accelerated the realization process of the autonomous driving goals. There are a number of autonomous driving initiatives around the world with varying objectives and scope, e.g. vehicle perception in a controlled environment or highway settings. Autonomous driving in a more complex environment with mixed traffic poses major challenges. The solutions for such environments is the focus of this paper. We start with a quick overview of current autonomous driving development activities worldwide. We then discuss the solution concept for autonomous driving in urban environments and its enabling components, e.g. road digitization and flexible communication infrastructure, to realize an urban autonomous driving testbed. We highlight the major challenges hindering the realization use-cases of Level 5 autonomous driving. Solution sketches to address these or similar changes are briefly discussed. We also implement some elements of the solution approaches on the real test-road. We demonstrate an artificial intelligence based approach for the analysis of real traffic data measured on the testbed. We implement approaches for predicting the network resource demands and allocation, which are crucial for realizing the use-cases of autonomous driving in complex environments. For the experiments, real data from the test-road is used. Results show that traffic patterns and resource demands are predicted accurately. These experiments are expected to instrumental for realizing other use-cases of autonomous driving.