Machine Learning, Autonomous Vehicles, Connected Vehicles, and High Performance Computing

Current Research

Learning-based predictive models and decision-making for connected and autonomous vehicles

(2019-present, collaborate with DENSO International American Inc.)

In this long-term and impactful project, I propose a learning-based framework along with corresponding algorithms to solve cutting-edge problems for connected and autonomous vehicles. Detailed topics include trajectory prediction for traffic agents, real-time driving aggressiveness probability prediction and learning-based motion planning. The project started in 2019 with DENSO, which is a high-prestige company working on intelligent transportation field.

At current stage, I have finished developing learning-based algorithms for trajectory prediction, and real-time driving aggressiveness prediction. We are deploying the algorithms in real-world systems in both Michigan and Ohio. The current phase of the project is focusing on prediction-based motion planning and decision-making for connected and autonomous vehicles.

Earlier Research (2017-2019)

Increasing GPS Localization Accuracy with Reinforcement Learning

RL-based algorithm performance vs. EKF

In this work, I propose a reinforcement learning framework to increase GPS localization accuracy. The framework does not make rigid assumptions on the GPS device hardware parameters or motion models, nor does it require infrastructure-based reference locations. It learns an optimal strategy to make “corrections” on raw GPS observations. The model uses an efficient confidence-based reward mechanism, which is independent of geolocation, thereby enabling the model to be generalized. We incorporate a map matching-based regularization term to reduce the variance of the reward return. The reinforcement learning model is constructed using the asynchronous advantage actor-critic (A3C) algorithm. The proposed reinforcement learning model converges fast, has less prediction variance, and can localize vehicles with 50% less error compared to the benchmark Extended Kalman Filter model.


V2XSim: A V2X Simulator for Connected and Automated Vehicle Environment Simulation

In this work we propose a vehicle-to-everything (V2X) simulator, called V2XSim, for connected vehicle environment simulation. The work provides an integrated V2X simulation platform, built with the Gazebo robot simulation engine. The simulated world is constructed with two types of models, namely dynamic and static models. We provide a detailed communication architecture to simulate the vehicle communication process. In this architecture, a closed-loop control module is built to pass control commands and control vehicle models without human intervention. We also provide multiple APIs for users to simplify the vehicle control process and make the simulator easy to use.

[Github, PDF]

GPU-Based Parallel Computing Algorithm for Real-time Mapping Between Large-scale Networks

In this work, I propose a scalable massively-parallel algorithm to solve the general mapping problem in large-scale networks in real-time. The proposed parallel algorithm takes advantage of GPU architecture and launches millions of workers to calculate values on a target network simultaneously. Threads are managed through the SIMT execution model and target values are updated through atomic operations. Experiments show the proposed algorithm can accomplish network mapping (find importance weights for links in a real-world large-scale shared-mobility network) with more than 2 million weights within 1.82 μs. Compared to serial algorithms, the speedup is more than 12,000 times.