Robust Accelerating Control for Consistent Node Dynamics in a Platoon of CAVs

Driving as a platoon has potential to significantly benefit traffic capacity and safety. To generate more identical dynamics of nodes for a platoon of automated connected vehicles (CAVs), this chapter presents a robust acceleration controller using a multiple model control structure. The large uncertainties of node dynamics are divided into small ones using multiple uncertain models, and accordingly multiple robust controllers are designed. According to the errors between current node and multiple models, a scheduling logic is proposed, which automatically selects the most appropriate candidate controller into loop. Even under relatively large plant uncertainties, this method can offer consistent and approximately linear dynamics, which simplifies the synthesis of upper level platoon controller. This method is validated by comparative simulations with a sliding model controller and a fixed H∞ controller

Effect of Pulse‐and‐Glide Strategy on Traffic Flow for a Platoon of Mixed Automated and Manually Driven Vehicles

The fuel consumption of ground vehicles is significantly affected by how they are driven. The fuel‐optimized vehicular automation technique can improve fuel economy for the host vehicle, but their effectiveness on a platoon of vehicles is still unknown. This article studies the performance of a well‐known fuel‐optimized vehicle automation strategy, i.e., Pulse‐and‐Glide (PnG) operation, on traffic smoothness and fuel economy in a mixed traffic flow. The mixed traffic flow is assumed to be a single‐lane highway on flat road consisting of both driverless and manually driven vehicles. The driverless vehicles are equipped with fuel economy‐oriented automated controller using the PnG strategy. The manually driven vehicles are simulated using the Intelligent Driver Models (IDM) to mimic the average car‐following behavior of human drivers in naturalistic traffics. A series of simulations are conducted with three scenarios, i.e., a single car, a car section, and a car platoon. The simulation results show that the PnG strategy can significantly improve the fuel economy of individual vehicles. For traffic flows, the fuel economy and traffic smoothness vary significantly under the PnG strategy.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/115907/1/mice12168.pd

Assessing the effectiveness of managed lane strategies for the rapid deployment of cooperative adaptive cruise control technology

Connected and Automated Vehicle (C/AV) technologies are fast expanding in the transportation and automotive markets. One of the highly researched examples of C/AV technologies is the Cooperative Adaptive Cruise Control (CACC) system, which exploits various vehicular sensors and vehicle-to-vehicle communication to automate vehicular longitudinal control. The operational strategies and network-level impacts of CACC have not been thoroughly discussed, especially in near-term deployment scenarios where Market Penetration Rate (MPR) is relatively low. Therefore, this study aims to assess CACC\u27s impacts with a combination of managed lane strategies to provide insights for CACC deployment. The proposed simulation framework incorporates 1) the Enhanced Intelligent Driver Model; 2) Nakagami-based radio propagation model; and 3) a multi-objective optimization (MOOP)-based CACC control algorithm. The operational impacts of CACC are assessed under four managed lane strategies (i.e., mixed traffic (UML), HOV (High Occupancy Vehicle)-CACC lane (MML), CACC dedicated lane (DL), and CACC dedicated lane with access control (DLA)). Simulation results show that the introduction of CACC, even with 10% MPR, is able to improve the network throughput by 7% in the absence of any managed lane strategies. The segment travel times for both CACC and non-CACC vehicles are reduced. The break-even point for implementing dedicated CACC lane is 30% MPR, below which the priority usage of the current HOV lane for CACC traffic is found to be more appropriate. It is also observed that DLA strategy is able to consistently increase the percentage of platooned CACC vehicles as MPR grows. The percentage of CACC vehicles within a platoon reaches 52% and 46% for DL and DLA, respectively. When it comes to the impact of vehicle-to-vehicle (V2V), it is found that DLA strategy provides more consistent transmission density in terms of median and variance when MPR reaches 20% or above. Moreover, the performance of the MOOP-based cooperative driving is examined. With average 75% likelihood of obtaining a feasible solution, the MOOP outperforms its counterpart which aims to minimize the headway objective solely. In UML, MML, and DL strategy, the proposed control algorithm achieves a balance spread among four objectives for each CACC vehicle. In the DLA strategy, however, the probability of obtaining feasible solution falls to 60% due to increasing size of platoon owing to DLA that constraints the feasible region by introduction more dimensions in the search space. In summary, UML or MML is the preferred managed lane strategy for improving traffic performance when MPR is less than 30%. When MRP reaches 30% or above, DL and DLA could improve the CACC performance by facilitating platoon formation. If available, priority access to an existing HOV lane can be adopted to encourage adaptation of CACC when CACC technology becomes publically available

Advanced Sensing and Control for Connected and Automated Vehicles

Connected and automated vehicles (CAVs) are a transformative technology that is expected to change and improve the safety and efficiency of mobility. As the main functional components of CAVs, advanced sensing technologies and control algorithms, which gather environmental information, process data, and control vehicle motion, are of great importance. The development of novel sensing technologies for CAVs has become a hotspot in recent years. Thanks to improved sensing technologies, CAVs are able to interpret sensory information to further detect obstacles, localize their positions, navigate themselves, and interact with other surrounding vehicles in the dynamic environment. Furthermore, leveraging computer vision and other sensing methods, in-cabin humans’ body activities, facial emotions, and even mental states can also be recognized. Therefore, the aim of this Special Issue has been to gather contributions that illustrate the interest in the sensing and control of CAVs

Experimental verification of vehicle platoon control algorithms

Organizing a group of vehicles into a vehicle platoon in a way that, except for the leading vehicle, each platoon member can be autonomously driven has been a research goal for decades. Among other benefits this results in a decrease of fuel consump- tion and also in the driver’s workload and an increase in a better use of road capacity. The recent developments in the area of ac- tive control systems for vehicles make it possible to realize more and more autonomous functions and the above defined cooper- ation between vehicles seems to be increasingly feasible. This article aims to point out that today it is possible to reach this goal without vehicle specific software and hardware