Prof. Mengchi Cai

Connected and automated vehicles have shown great potential in improving traffic mobility and reducing emissions, especially at unsignalized intersections. Previous research has shown that vehicle passing order is the key influencing factor in improving intersection traffic mobility. In this paper, we propose a graph-based cooperation method to formalize the conflict-free scheduling problem at an unsignalized intersection. Based on graphical analysis, a vehicle’s trajectory conflict relationship is modeled as a conflict directed graph and a coexisting undirected graph. Then, two graph-based methods are proposed to find the vehicle passing order. The first is an improved depth-first spanning tree algorithm, which aims to find the local optimal passing order vehicle by vehicle. The other novel method is a minimum clique cover algorithm, which identifies the global optimal solution. Finally, a distributed control framework and communication topology are presented to realize the conflict-free cooperation of vehicles. Extensive numerical simulations are conducted for various numbers of vehicles and traffic volumes, and the simulation results prove the effectiveness of the proposed algorithms.

7 February 2023 in IEEE Transactions on Intelligent Vehicles

Journal Article

Mixed Cloud Control Testbed: Validating Vehicle-Road-Cloud Integration via Mixed Digital Twin

Qing Xu

Chunying Yang

Reliable and efficient validation technologies are critical for the recent development of multi-vehicle cooperation and vehicle-road-cloud integration. In this paper, we introduce our miniature experimental platform, Mixed Cloud Control Testbed (MCCT), developed based on a new notion of Mixed Digital Twin (mixedDT). Combining Mixed Reality with Digital Twin, mixedDT integrates the virtual and physical spaces into a mixed one, where physical entities coexist and interact with virtual entities via their digital counterparts. Under the framework of mixedDT, MCCT contains three major experimental platforms in the physical, virtual and mixed spaces respectively, and provides a unified access for various human-machine interfaces and external devices such as driving simulators. A cloud unit, where the mixed experimental platform is deployed, is responsible for fusing multi-platform information and assigning control instructions, contributing to synchronous operation and real-time cross-platform interaction. Particularly, MCCT allows for multi-vehicle coordination composed of different multi-source vehicles ( e.g. , physical vehicles, virtual vehicles and human-driven vehicles). Validations on vehicle platooning demonstrate the flexibility and scalability of MCCT.

23 March 2022 in IEEE Transactions on Vehicular Technology

Journal Article

Multi-Lane Unsignalized Intersection Cooperation With Flexible Lane Direction Based on Multi-Vehicle Formation Control

Qing Xu

Unsignalized intersection cooperation of connected and automated vehicles (CAVs) is able to eliminate green time loss of signalized intersections and improve traffic efficiency. Most of the existing research on unsignalized intersection cooperation considers fixed lane direction, where only specific turning behavior of vehicles is allowed on each lane. Given that traffic volume and the proportion of vehicles with different turning expectation may change with time, fixed lane direction may lead to inefficiency at intersections. This paper proposes a multi-lane unsignalized intersection cooperation method that considers flexible lane direction. The two-dimensional distribution of vehicles is calculated and vehicles that are not in conflict are scheduled to pass the intersection simultaneously. The formation reconfiguration method is utilized to achieve collision-free longitudinal and lateral position adjustment of vehicles. Simulations are conducted at different input traffic volumes and turning proportion of incoming vehicles, and the results indicate that our method outperformances the fixed-lane-direction unsignalized cooperation method and the signalized method.

8 August 2023 in IEEE Internet of Things Journal

Journal Article

Implementation and Experimental Validation of Data-Driven Predictive Control for Dissipating Stop-and-Go Waves in Mixed Traffic

Qing Xu

In this article, we present the first experimental results of data-driven predictive control for connected and autonomous vehicles (CAVs) in dissipating traffic waves. In particular, we consider a recent strategy of Data-EnablEd Predictive Leading Cruise Control ( DeeP-LCC ), which bypasses the need of identifying the driving behaviors of surrounding vehicles and directly relies on measurable traffic data to achieve safe and optimal CAV control in mixed traffic. We present the implementation details of DeeP-LCC , including data collection, equilibrium estimation, and control execution. Based on a miniature experiment platform, we reproduce the phenomenon of stop-and-go waves in two typical traffic scenarios: 1) open straight-road scenario under external disturbances and 2) closed ring-road scenario with no bottlenecks. Our experiments clearly demonstrate that DeeP-LCC enables one or a few CAVs to dissipate the traffic waves in both traffic scenarios. These experimental findings validate the great potential of DeeP-LCC in smoothing practical traffic flow in the presence of noisy data, uncertain low-level vehicle dynamics, and communication and computation delays. The code and videos of our experimental results are available at https://github.com/soc-ucsd/DeeP-LCC .

22 July 2022 in IEEE Transactions on Vehicular Technology

Journal Article

Cooperation Method of Connected and Automated Vehicles at Unsignalized Intersections: Lane Changing and Arrival Scheduling

Chaoyi Chen

Mengchi Cai

Jiawei Wang

Kai Li

Qing Xu

Keqiang Li

Connected and automated vehicles (CAVs) have shown great potential in improving traffic efficiency in the area of intersection management. Numerous researches have been accomplished to solve the CAV scheduling problem at the intersection scenario. However, most of them focus on intersections where lane-changing behavior is prohibited, which is impractical for real-world implementation. To address this problem, this paper proposes a novel cooperation framework to solve the CAV arrival scheduling problem in unsignalized intersections where lane changing behavior is permitted. In the first stage, an iterative framework is proposed to solve target assignment and path planning problems, which prevents the deadlock of multiple CAVs with different target lanes. In the second stage, a graph-based minimum clique cover method is then established to obtain the optimal scheduling plan for the CAVs with a low calculation burden. Extensive numerical simulations for different numbers of vehicles and traffic volumes validate the effectiveness of the proposed algorithm.

23 December 2020 in IET Intelligent Transport Systems

Journal Article

Coordinated formation control for intelligent and connected vehicles in multiple traffic scenarios

Qing Xu

Mengchi Cai

Keqiang Li

Biao Xu

Jianqiang Wang

Xiangbin Wu

In this paper, a unified multi‐vehicle formation control framework for intelligent and connected vehicles (ICVs) that can apply to multiple traffic scenarios is proposed. In the one‐dimensional scenario, different formation geometries are analysed, and the interlaced structure is mathematically modellised to improve driving safety while making full use of the lane capacity. The assignment problem for vehicles, and target positions is solved using Hungarian algorithm to improve the flexibility of the method in multiple scenarios. In the two‐dimensional scenario, an improved virtual platoon method is proposed to transfer the complex two‐dimensional passing problem to the one‐dimensional formation control problem based on the idea of rotation projection. Besides, the vehicle regrouping method is proposed to connect the two scenarios. Simulation results prove that the proposed multi‐vehicle formation control framework can apply to multiple typical scenarios, and have better performance than existing methods.

About Me

Mengchi Cai received his B.E. degree and Ph. D. degree from Tsinghua University, Beijing, China, in 2018 and 2023, respectively. He currently serves as an Assistant Research Professor at Tsinghua University. Dr. Cai was selected for the Young Elite Scientists Sponsorship Program by China Association for Science and Technology, and received support from Tsinghua University's "Shuimu Tsinghua Scholarship" program. He achieved first place nationally in the "Perception-Decision-Control Comprehensive Competition" of the 2024 OnSite Autonomous Driving Algorithm Challenge Competition organized by the National Natural Science Foundation of China (NSFC). He has contributed to the approval of one ITU international standard and one group standard of the China Society of Automotive Engineers. Dr. Cai has led six projects, including the Young Scientists Fund of NSFC (Category C), the Outstanding Funding from China Postdoctoral Science Foundation, an international collaboration project with Mercedes-Benz, and a collaborative project with BYD Automotive. He has published over 30 papers, received the Best Youth Paper Award at an international conference, applied for more than 20 invention patents, and been granted over 10 patents. His research areas include intelligent and connected vehicles and transportation, vehicle-road-cloud integrated systems and their applications, and multi-vehicle formation control.

Prof. Mengchi Cai

Prof. Mengchi Cai

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