8 citations · 12 across the 10 of their papers we have counts for
11 papers
Safety-Assured Speculative Planning with Adaptive Prediction
Xiangguo Liu, Ruochen Jiao, Yixuan Wang +3
Recently significant progress has been made in vehicle prediction and planning algorithms for autonomous driving. However, it remains quite challenging for an autonomous vehicle to…
Learning Representation for Anomaly Detection of Vehicle Trajectories
Ruochen Jiao, Juyang Bai, Xiangguo Liu +4
Predicting the future trajectories of surrounding vehicles based on their history trajectories is a critical task in autonomous driving. However, when small crafted perturbations a…
Interactive Trajectory Planner for Mandatory Lane Changing in Dense Non-Cooperative Traffic
Xiangguo Liu, Jianxing Chen, Shan Li +7
When the traffic stream is extremely congested and surrounding vehicles are not cooperative, the mandatory lane changing can be significantly difficult. In this work, we propose an…
Connectivity Enhanced Safe Neural Network Planner for Lane Changing in Mixed Traffic
Xiangguo Liu, Ruochen Jiao, Bowen Zheng +2
Connectivity technology has shown great potentials in improving the safety and efficiency of transportation systems by providing information beyond the perception and prediction ca…
DSRC & C-V2X Comparison for Connected and Automated Vehicles in Different Traffic Scenarios
Yuanzhe Jin, Xiangguo Liu, Qi Zhu
Researches have been devoted to making connected and automated vehicles (CAVs) faster in different traffic scenarios. By using C-V2X or DSRC communication protocol, CAVs can work m…
TAE: A Semi-supervised Controllable Behavior-aware Trajectory Generator and Predictor
Ruochen Jiao, Xiangguo Liu, Bowen Zheng +2
Trajectory generation and prediction are two interwoven tasks that play important roles in planner evaluation and decision making for intelligent vehicles. Most existing methods fo…
Physics-Aware Safety-Assured Design of Hierarchical Neural Network based Planner
Xiangguo Liu, Chao Huang, Yixuan Wang +2
Neural networks have shown great promises in planning, control, and general decision making for learning-enabled cyber-physical systems (LE-CPSs), especially in improving performan…
Safety-driven Interactive Planning for Neural Network-based Lane Changing
Xiangguo Liu, Ruochen Jiao, Bowen Zheng +2
Neural network-based driving planners have shown great promises in improving task performance of autonomous driving. However, it is critical and yet very challenging to ensure the…
End-to-end Uncertainty-based Mitigation of Adversarial Attacks to Automated Lane Centering
Ruochen Jiao, Hengyi Liang, Takami Sato +3
In the development of advanced driver-assistance systems (ADAS) and autonomous vehicles, machine learning techniques that are based on deep neural networks (DNNs) have been widely…
Securing Connected Vehicle Applications with an Efficient Dual Cyber-Physical Blockchain Framework
Xiangguo Liu, Baiting Luo, Ahmed Abdo +2
While connected vehicle (CV) applications have the potential to revolutionize traditional transportation system, cyber and physical attacks on them could be devastating. In this wo…
Trajectory Planning for Connected and Automated Vehicles: Cruising, Lane Changing, and Platooning
Xiangguo Liu, Guangchen Zhao, Neda Masoud +1
Autonomy and connectivity are considered among the most promising technologies to improve safety, mobility, fuel and time consumption in transportation systems. Some of the fuel ef…