This perspective paper delves into the concept of foundation intelligence that shapes the future of smart infrastructure services as the transportation sector transitions into the era of Transportation 5.0. First, the discussion focuses on a suite of emerging technologies essential for foundation intelligence. These technologies encompass digital twinning, parallel intelligence, large vision-language models, traffic simulation and transportation systems modeling, vehicle-to-everything (V2X) connectivity, and decentralized/distributed systems. Next, the paper introduces the present landscape of Transportation 5.0 applications as illuminated by the foundational intelligence, and casts a vision towards the future including cooperative driving automation, smart intersection/infrastructure, parallel traffic management, virtual drivers, and mobility systems planning and operations, laying out prospects that are poised to redefine the mobility ecosystem. Last, through a comprehensive outlook, this paper aspires to offer a guiding framework for the intelligent evolution in data generation and model calibration, digital twinning and simulation, scenario development and experimentation, feedback loop for management and control, and continuous learning and adaptation, fostering safety, efficiency, reliability, and sustainability in the future smart transportation infrastructure.

Autonomous driving (AD) and Cooperative Driving Automation (CDA) hold great promise for transforming mobility. However, current off-the-shelf AD or CDA platforms such as Autoware, Apollo, and CARMA, are subject to gaps between simulation and the real world and do not offer integrated pipelines for CDA research, development, and deployment. In this letter, we conceptualize OpenCDA-ROS, building on the strengths of an open-source framework OpenCDA and the Robot Operating System (ROS), to seamlessly synthesize ROS's real-world deployment capabilities with OpenCDA's mature CDA research framework and simulation-based evaluation to fill the gaps aforementioned. OpenCDA-ROS will leverage the advantages of both ROS and OpenCDA to boost the prototyping and deployment of critical CDA features in both simulation and the real world, particularly for cooperative perception, mapping and digital twinning, cooperative decision-making and motion planning, and smart infrastructure services. By offering seamless integration of simulation and real-world CDA, OpenCDA-ROS contributes significantly to advancing fundamental research, development, testing, validation, prototyping, and deployment of autonomous driving and CDA.

Accurate vehicle pose is fundamental information required by automated driving systems. However, complicated driving environments and sensor failures have constrained onboard sensor-based single-vehicle localization precision. With the development of cooperative driving automation, the information from surrounding vehicles in the Vehicle-to-Everything (V2V) network offers remarkable potential to boost the ego vehicle’s localization performance. In this paper, we propose a cooperative vehicle localization framework based on multi-sensor fusion that uses shared information from multi-agents, leveraging point cloud feature matching and object detection. The ego vehicle’s detection system can determine the relative pose between the ego vehicle and the corresponding surrounding vehicles based on data from the LiDAR sensor. However, the accuracy of the pose information derived directly from deep-learning-based object detection is limited. Thus, a relative pose refining method is proposed to further improve the relative pose by applying a point cloud matching technique based on a normal distribution transformation approach. Meanwhile, to reduce the data transmission load, we extract only the edge and plane features from the surrounding vehicle’s LiDAR scan and exclude the remaining point cloud. Additionally, the shared information is fused into the ego vehicle’s INS-based localization system, which enables continuous and high-frequency localization output within a Kalman filter framework. To make the fusion algorithm more adaptive to different relative pose noise levels, a measurement quality evaluation rule is designed. Real-world vehicular experiments show that the proposed algorithm can improve localization accuracy by at least 35% compared to the traditional range-based cooperative localization method.

In the era of future mobility within Transportation 5.0, autonomy and cooperation across all road users and smart infrastructure stand as the key features to enhance transportation safety, efficiency, and sustainability, supported by cooperative perception, decision-making and planning, and control. An accurate and robust localization system plays a vital role in enabling these modules for future mobility and is constrained by environmental uncertainties and sensing limitations. To achieve precise and resilient localization in this new era, this paper introduces emerging technologies including edge computing, hybrid data-driven and physical model approaches, foundation models as well as parallel intelligence, that are beneficial for next-generation localization systems. On top of these key technologies, by integrating real-world testing and digital twin technology, we further put forward a Decentralized Autonomous Service (DAS)-based cooperative localization framework for future mobility systems to enhance the resilience, robustness, and safety of transportation systems.