Connected Autonomous Vehicles have great potential to improve automobile safety and traffic flow, especially in cooperative applications where perception data is shared between vehicles. However, this cooperation must be secured from malicious intent and unintentional errors that could cause accidents. Previous works typically address singular security or reliability issues for cooperative driving in specific scenarios rather than the set of errors together. In this paper, we propose CONClave - a tightly coupled authentication, consensus, and trust scoring mechanism that provides comprehensive security and reliability for cooperative perception in autonomous vehicles. CONClave benefits from the pipelined nature of the steps such that faults can be detected significantly faster and with less compute. Overall, CONClave shows huge promise in preventing security flaws, detecting even relatively minor sensing faults, and increasing the robustness and accuracy of cooperative perception in CAVs while adding minimal overhead.

5G Millimeter Wave (mmWave) technology holds great promise for Connected Autonomous Vehicles (CAVs) due to its ability to achieve data rates in the Gbps range. However, mmWave suffers from a high beamforming overhead and requirement of line of sight (LOS) to maintain a strong connection. For Vehicle-to-Infrastructure (V2I) scenarios, where CAVs connect to roadside units (RSUs), these drawbacks become apparent. Because vehicles are dynamic, there is a large potential for link blockages. These blockages are detrimental to the connected applications running on the vehicle, such as cooperative perception and remote driver takeover. Existing RSU selection schemes base their decisions on signal strength and vehicle trajectory alone, which is not enough to prevent the blockage of links. Many modern CAVs motion planning algorithms routinely use other vehicle’s near-future path plans, either by explicit communication among vehicles, or by prediction. In this paper, we make use of the knowledge of other vehicle’s near future path plans to further improve the RSU association mechanism for CAVs. We solve the RSU association algorithm by converting it to a shortest path problem with the objective to maximize the total communication bandwidth. We evaluate our approach, titled B-AWARE, in simulation using Simulation of Urban Mobility (SUMO) and Digital twin for self-dRiving Intelligent VEhicles (DRIVE) on 12 highway and city street scenarios with varying traffic density and RSU placements. Simulations show B-AWARE results in a 1.05× improvement of the potential datarate in the average case and 1.28× in the best case vs. the state-of-the-art. But more impressively, B-AWARE reduces the time spent with no connection by 42% in the average case and 60% in the best case as compared to the state-of-the-art methods. This is a result of B-AWARE reducing nearly 100% of blockage occurrences.

Different types of lane-changing assistance systems are usually developed separately by different automotive makers or suppliers. A lane-changing model can meet its own requirements, but it may be incompatible with another lane-changing model. In this paper, we verify if two lane-changing models are compatible so that the two corresponding vehicles on different lanes can exchange their lanes successfully. We propose a methodology and an algorithm to perform the verification on the combinations of four lane-changing models. Experimental results demonstrate the compatibility (or incompatibility) between the models. The verification results can be utilized during runtime to prevent incompatible vehicles from entering a lane-changing road segment. To the best of our knowledge, this is the first work considering the compatibility issue for lane-changing models.

Many Cyber-Physical Systems (CPS) have timing constraints that must be met by the cyber components (software and the network) to ensure safety. It is a tedious job to check if a CPS meets its timing requirement especially when it is distributed and the software and/or the underlying computing platforms are complex. Furthermore, the system design is brittle since a timing failure can still happen (e.g., network failure, soft error bit flip). In this article, we propose a new design methodology calledPlan Bwhere timing constraints of the CPS are monitored at runtime, and a proper backup routine is executed when a timing failure happens to ensure safety. We provide a model on how to express the desired timing behavior using a set of timing constructs in a C/C++ code and how to efficiently monitor them at the runtime. We showcase the effectiveness of our approach by conducting experiments on three case studies: (1) the full software stack for autonomous driving (Apollo), (2) a multi-agent system with 1/10th-scale model robots, and (3) a quadrotor for search and rescue application. We show that the system remains safe and stable even when intentional faults are injected to cause a timing failure. We also demonstrate that the system can achieve graceful degradation when a less extreme timing failure happens.

Intersection management of Connected Autonomous Vehicles (CAVs) has the potential to improve safety and mobility. CAVs approaching an intersection can exchange information with the infrastructure or each other to schedule their cross times. By avoiding unnecessary stops, scheduling CAVs can increase traffic throughput, reduce energy consumption, and most importantly, minimize the number of accidents that happen in intersection areas due to human errors. We study existing intersection management approaches from following key perspectives: (1) intersection management interface, (2) scheduling policy, (3) existing wireless technologies, (4) existing vehicle models used by researchers and their impact, (5) conflict detection, (6) extension to multi-intersection management, (7) challenges of supporting human-driven vehicles, (8) safety and robustness required for real-life deployment, (9) graceful degradation and recovery for emergency scenarios, (10) security concerns and attack models, and (11) evaluation methods. We then discuss the effectiveness and limitations of each approach with respect to the aforementioned aspects and conclude with a discussion on tradeoffs and further research directions.

For autonomous vehicles, intelligent autonomous intersection management will be required for safe and efficient operation. In order to achieve safe operation despite uncertainties in vehicle trajectory, intersection management techniques must consider a safety buffer around the vehicles. For truly safe operation, an extra buffer space should be added to account for the network and computational delay caused by communication with the Intersection Manager (IM). However, modeling the worst-case computation and network delay as additional buffer around the vehicle degrades the throughput of the intersection. To avoid this problem, AIM[1], a popular state-of-the-art IM, adopts a query-based approach in which the vehicle requests to enter at a certain arrival time dictated by its current velocity and distance to the intersection, and the IM replies yes/no. Although this solution does not degrade the position uncertainty, it ultimately results in poor intersection throughput. We present Crossroads, a time-sensitive programming method to program the interface of a vehicle and the IM. Without requiring additional buffer to account for the effect of network and computational delay, Crossroads enables efficient intersection management. Test results on a 1/10 scale model of intersection using TRAXXAS RC cars demonstrates that our Crossroads approach obviates the need for large buffers to accommodate for the network and computation delay, and can reduce the average wait time for the vehicles at a single-lane intersection by 24%. To compare Crossroads with previous approaches, we perform extensive Matlab simulations, and find that Crossroads achieves on average 1.62X higher throughput than a simple VT-IM with extra safety buffer, and 1.36X better than AIM.