Browsing by Author "Saripalli, Srikanth"

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    Development of a Roadside LiDAR-Based Situational Awareness System for Work Zone Safety: Proof-of-Concept Study
    Wu, Jayson (Dayong); Le, Minh; Ullman, Jerry; Huang, Tianchen; Darwesh, Amir; Saripalli, Srikanth (Safe-D University Transportation Center, 2023-09)
    Roadway construction and maintenance have become increasingly common as the U.S. transportation system ages and the population and traffic volume increase. This places more and more work zone workers near high-speed vehicles and increases the probability of being struck by them. This project innovatively deployed 360-degree LiDAR sensors at the roadside and tested their potential to provide work zone safety in terms of detection accuracy, efficiency, and ease of use. Researchers developed a set of algorithms to collect and interpret real-time information for each approaching vehicle and worker (e.g., location, speed, and direction) in and outside work zones using roadside LiDAR. Ultimately, the outcome of this pilot study could lead to developing a full-scale warning system deployable in a real work zone environment. Such a system could detect and analyze live traffic and work zone activity, activate the appropriate warning scheme, and deliver information to roadway workers in work zones in a timely manner so they can take evasive actions instead of relying on traditional “passive” safety countermeasures. This kind of panoramic, trajectory-level data for work zone actors can be used to develop a next-generation work zone situational awareness system.
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    Formalizing Human Machine Communication in the Context of Autonomous Vehicles
    Gopalswamy, Swaminathan; Saripalli, Srikanth; Shell, Dylan; Hickman, Jeffrey S.; Hsu, Ya-Chuan (SAFE-D: Safety Through Disruption National University Transportation Center, 2020-05)
    There are many situations where tacit communication between drivers and pedestrians governs and enhances safety. The goal of this study was to formalize this communication and apply it to the driving strategy of an autonomous vehicle. Toward this, we performed a field study of the interaction between drivers and pedestrians. Vehicles were instrumented to capture behavioral information on a driver as well as passengers and the traffic scenario in general. The data captured were reduced by data analysts to provide insights into the communication and driving patterns. The categorical reduction on driver, pedestrian, and environmental variables was captured. A domain specific language (DSL) was developed to precisely describe the driver-pedestrian behavior, toward the development of a behavioral model for generating autonomous vehicle controls. Specifically, interaction was formalized through a probabilistic model, namely a partially observable Markov decision process (POMDP). This enabled study of what-if scenarios with different risk averseness characteristics. One particular strategy was implemented on an autonomous vehicle and experimental observations were made. Future work will consider (i) richer DSLs to better quantify the driver-human communication, (ii) faster POMDP solvers for real-time operation, and (iii) further applications.