The emergence of autonomous driving technologies has led to the development of specialized testbeds like ICAT. ICAT addresses limitations in indoor autonomous driving by innovating vehicle computing and V2X communication. Leveraging digital twins through CARLA and SUMO simulations, ICAT facilitates both centralized and decentralized autonomy deployments. The platform aims to enhance research in navigation, traffic optimization, and swarm intelligence by providing a scalable and cost-effective solution for indoor testing scenarios.
ICAT stands out due to its emphasis on V2X capability, supporting inter-vehicle, vehicle-infrastructure, and vehicle-server communications. It also integrates with CARLA and SUMO simulations for centralized and decentralized autonomy deployments. The platform's digital twin system allows for faster algorithm iteration by facilitating efficient simulations.
The paper discusses the motivation behind building ICAT as an alternative to expensive outdoor testbeds, highlighting challenges faced in previous studies related to localization accuracy, onboard computing power, and simulation capabilities. The design of ICAT is detailed across various aspects such as digital twin technology, infrastructure integration, localization methods, traffic management systems, decentralized autonomous driving approaches, vehicle computing capabilities, multi-user management strategies, auto-recharge functionalities for robots like HydraT platform.
Two case studies are presented to evaluate the efficacy of the ICAT platform: operational integrity of the traffic management system and execution of federated machine learning tasks. Insights gained from these studies include challenges related to NDT localization noise impact on pose initialization, control issues in trajectory tracking using pure-pursuit controller method, and effects of communication lag on response speed.
Future work includes optimizing localization accuracy with better filtering techniques, implementing model predictive control methods for minimizing trajectory tracking errors, developing accurate spatial-temporal environment dynamic models based on real-time data collection. Additionally, enhancing communication devices' bandwidth at the hardware end will be explored along with investigating task scheduling strategies considering lag-considered safety protection measures.
The paper concludes by emphasizing how ICAT has demonstrated proficiency in managing simulated traffic systems while executing complex federated ML tasks. The platform's integration of connectivity features with advanced onboard computing devices positions it as a valuable tool for modern intelligent transportation research.
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