Sensing and Data Acquisition Techniques

The advent of connected vehicles (CVs) and the progression toward full autonomy have positioned modern automobiles as complex mobile sensing platforms. Relying on a sophisticated array of sensors—including cameras, LiDARs, radars, IMUs, and GPS—these vehicles perceive and interact with their surroundings with remarkable accuracy. This chapter delves into the multifaceted sensing and data acquisition techniques pivotal to the operation of CVs, examining the roles and integration of various automotive sensors and the challenges they present. It is estimated that a single autonomous vehicle can generate between 20 to 40 terabytes of data each day, including data streams from cameras, sonars, radars, and LiDARs, highlighting the critical necessity for effective data management strategies. Addressing the consequent “data explosion,” this chapter provides insight into the state-of-the-art in data storage, compression, and logging systems tailored for vehicular contexts, which are imperative for enhancing the performance and security of the overall vehicle computing system. Moreover, it investigates the synchronization issues associated with multi-sensor data and proposes solutions for handling anomalous situations such as adverse weather conditions, emergency maneuvers, and challenging work zones. By offering a comprehensive overview of the current challenges and deliberating on future directions, this chapter lays the groundwork for researchers and practitioners to advance the domain of vehicle computing, ensuring secure and efficient management of sensor data.