Chung-Wei Lin

In modern vehicular systems, substantial computation is required for functions such as high-definition mapping, object detection, and driving assistance. While onboard computing offers low-latency access, it is sometimes insufficient in capacity. Cloud computing provides scalability but suffers from latency issues. Edge computing offers a balance of low-latency, localized service delivery, and enhanced computational capacity, but it demands sophisticated allocation and delivery mechanisms. In this paper, we address the service allocation problem in edge-assisted vehicular networks under realistic assumptions of variable but bounded vehicle velocities. We design efficient computation allocation algorithms and introduce a speed-adjustment suggestion mechanism for vehicles not immediately assigned resources, allowing them to slow down and increase future allocation chances. Moreover, we propose a dedicated scheduling strategy tailored to normal traffic and platoon-based traffic, leveraging their structural properties. To mitigate computational overhead, we develop a novel and scalable approximation technique. Experimental evaluations across various traffic scenarios demonstrate the effectiveness, scalability, and real-time applicability of the proposed methods. Our code is available at https://github.com/AAAA-source/Edge-Assisted-Service-Allocation-for-Connected-Vehicles-with-Variable-Velocities .

Vehicle platoon merging presents significant challenges due to varying speeds, gap estimation errors, and synchronization issues. Heterogeneous controllers, in particular, can cause mismatches in control dynamics, resulting in inconsistent acceleration responses, unstable gap spacing, and unpredictable behaviors that negatively impact user comfort and safety. This work investigates the merging performance of 25 controller combinations, pairing five controllers and testing them across three merging scenarios, with a focus on both user comfort and safety gaps. We identify key trade-offs: mismatches between reactive and predictive controllers often lead to higher jerk, and some joining controllers experience elevated jerk during merging with braking disturbances, resulting in poor comfort. To address these issues, we propose an adaptive transitory controller—a simple assistance module for the joining platoon leader—that enhances compatibility with various preceding controllers and merging scenarios. Results show consistent performance and a 58.38% improvement in user comfort while maintaining gap safety. We also discuss the limitations of our approach and propose directions for future work to further improve robustness.

Cooperative Adaptive Cruise Control (CACC) is a fundamental connected vehicle application. In CACC, a vehicle coordinates its longitudinal movements to safely and efficiently follow the vehicle in front. The follower vehicle relies on a combination of sensory and communication inputs to identify the position, velocity, and acceleration of the preceding vehicle. Malicious subversion of these inputs can cause catastrophic accidents, string instability, and disruption in the transportation infrastructure. In this paper, we develop a security system, $\recap$ , to provide real-time resiliency in CACC against adversarial subversion of both sensory and communication inputs. $\recap$ makes use of a combination of techniques based on kinematics and machine learning to detect anomalous inputs, narrow down the source of subversion, and perform mitigation. We provide extensive simulations to demonstrate the effectiveness of $\recap$ against a diverse spectrum of attacks under complex, multi-channel adversaries.

Connected Autonomous Vehicles (CAVs) are expected to enable reliable, efficient, and intelligent transportation systems. Most motion-planning algorithms for multi-agent systems implicitly assume that all vehicles/agents will execute the expected plan with a small error and evaluate their safety constraints based on this fact. This assumption, however, is hard to keep for CAVs since they may have to change their plan (e.g., to yield to another vehicle) or are forced to stop (e.g., a CAV may break down). While it is desired that a CAV never gets involved in an accident, it may be hit by other vehicles and, sometimes, preventing the accident is impossible (e.g., getting hit from behind while waiting at a red light). Responsibility-Sensitive Safety (RSS) is a set of safety rules that defines the objective of CAVs to blame, instead of safety. Thus, instead of developing a CAV algorithm that will avoid any accident, it ensures that the ego vehicle will not be blamed for any accident it is a part of. Original RSS rules, however, are hard to evaluate for merge, intersection, and unstructured road scenarios, plus RSS rules do not prevent deadlock situations among vehicles. In this article, we propose a new formulation for RSS rules that can be applied to any driving scenario. We integrate the proposed RSS rules with the CAV’s motion planning algorithm to enable cooperative driving of CAVs. We use Control Barrier Functions to enforce safety constraints and compute the energy optimal trajectory for the ego CAV. Finally, to ensure liveness, our approach detects and resolves deadlocks in a decentralized manner. We have conducted different experiments to verify that the ego CAV does not cause an accident no matter when other CAVs slow down or stop. We also showcase our deadlock detection and resolution mechanism using our simulator. Finally, we compare the average velocity and fuel consumption of vehicles when they drive autonomously with the case that they are autonomous and connected.

Intersection management systems, with the assistance of vehicular networks and autonomous vehicles, have the potential to perform traffic control more precisely than contemporary signalized intersections. However, as infrastructural intersection management controllers do not directly activate motions of vehicles, it is possible that the vehicles fail to follow the instructions from controllers, undermining system properties such as deadlock-freeness and traffic performance. In this article, we consider a class of robustness issues, the time violations, which stem from possible discrepancies between scheduled orders and real executions. We refine a graph-based intersection model to build our theoretical foundations and analyze potential deadlocks and their resolvability. We develop solutions that mitigate negative effects of time violations. In particular, we propose a Robustness-Aware Greedy Scheduling algorithm for robust scheduling and evaluate the deadlock-free robustness of different intersection models and scheduling algorithms. Experimental results show that the Robustness-Aware Greedy Scheduling algorithm is able to significantly improve robustness and keep a good balance with traffic performance.

As vehicular technology advances, vehicles become more connected and autonomous. Connectivity provides the capability to exchange information between vehicles, and autonomy provides the capability to make decisions and control each vehicle precisely. Connectivity and autonomy realize many evolutional applications, such as intelligent intersection management and cooperative adaptive cruise control. Electric vehicles are sometimes combined to create more use cases and business models. However, these intelligent features make the design process more complicated and challenging. In this talk, we introduce several examples of automotive design automation, which is required to improve the design quality and facilitate the design process. We mainly discuss the rising incompatibility issue, where different original equipment manufacturers and suppliers are developing systems, but the designs are confidential and thus incompatible with other players' designs. The incompatibility issue is especially critical with autonomous vehicles because no human driver resolves incompatible scenarios. We believe that techniques and experiences in electronic design automation can provide insights and solutions to automotive design automation.