I'm a second-year Ph.D. candidate majoring in Computer Science and Research Assistant in State University of New York (SUNY) at Binghamton, USA. My research focus on Task and Motion Planning (TAMP): Intersection of Planning from Artificial Intelligence and Motion Planning from Robotics (including vehicles). I'm supervised by Assistant Professor Shiqi Zhang.
Autonomous vehicles need to plan at the task level to compute a sequence of symbolic actions, such as merging left and turning right, to fulfill people's service requests, where efficiency is the main concern. At the same time, the vehicles must compute continuous trajectories to perform actions at the motion level, where safety is the most important. Task-motion planning in autonomous driving faces the problem of maximizing task-level efficiency while ensuring motion-level safety. To this end, we develop algorithm Task-Motion Planning for Urban Driving (TMPUD) that, for the first time, enables the task and motion planners to communicate about the safety level of driving behaviors. TMPUD has been evaluated using a realistic urban driving simulation platform. Results suggest that TMPUD performs significantly better than competitive baselines from the literature in efficiency, while ensuring the safety of driving behaviors.
An increasing number of vehicles are now equipped with GPS devices to facilitate fleet management and send their GPS locations continuously, generating a huge volume of trajectory data. Sending and storing such vehicle trajectory data cause sustainable communication and storage overheads. Trajectory data compression becomes a promising way to alleviate overhead issues. However, previous solutions are commonly carried out at the side of the data center after data having been received, thus saving the storage cost only. Here, we bring the idea of mobile edge computing and transfer the computation-intensive data compression task to the mobile devices of drivers. As a result, the trajectory data is reduced at the side of data generators before being sent out; thus, it can lower data communication and storage costs simultaneously. We propose DAVT, an error-bounded trajectory data representation, and a compression framework. Specifically, the trajectory data is reformatted into three parts (i.e., Distance, Acceleration & Velocity, and Time), and three compressors are wisely devised to compress each part. For D and AV parts, a similar Huffman tree-forest structure is exploited to encode data elements effectively, but with quite different rationales. For the T part, the large absolute timestamps are transformed to small time intervals firstly, and different encoding techniques are adopted based on the data quality. We evaluate our proposed system using a large-scale taxi trajectory dataset collected from the city of Beijing, China. Our results show that our compressors outperform other baselines.
The rapidly-growing business of ride-on-demand (RoD) service such as Uber, Lyft and Didi proves the effectiveness of their new service model – using mobile apps and dynamic pricing to coordinate between drivers, passengers and the service provider, to manipulate the supply and demand, and to improve service responsiveness as well as quality. Despite its success, dynamic pricing creates a new problem for drivers: how to seek for passengers to maximize revenue under dynamic prices. Seeking route recommendation has already been studied extensively in traditional taxi service, but most studies do not consider the effects of taxis and passengers on the seeking taxi simultaneously. Further, in RoD service it is necessary to consider more factors such as dynamic prices, the status of other transportation services, etc. In this paper, we employ a force-directed approach to model, by analogy, the relationship between vacant cars and passengers as that between positive and negative charges in electrostatic field. We extract features from multi-source urban data to describe dynamic prices, the status of RoD, taxi and public transportation services, and incorporate them into our model. The model is then used in route recommendation in every intersection so that a driver in a vacant RoD car knows which road segment to take next. We conduct extensive experiments based on our multi-source urban data, including RoD service operational data, taxi GPS trajectory data and public transportation distribution data, and results not only show that our approach outperforms existing baselines, but also justify the need to incorporate multi-source urban data and dynamic prices.