Distributed H∞ Controller Design and Robustness Analysis for Vehicle Platooning Under Random Packet Drop

This paper presents the design of a robust distributed state-feedback controller in the discrete-time domain for homogeneous vehicle platoons with undirected topologies, whose dynamics are subjected to external disturbances and under random single packet drop scenario. A linear matrix inequality (LMI) approach is used for devising the control gains such that a bounded H∞ norm is guaranteed. Furthermore, a lower bound of the robustness measure, denoted as γ gain, is derived analytically for two platoon communication topologies, i.e., the bidirectional predecessor following (BPF) and the bidirectional predecessor leader following (BPLF). It is shown that the γ gain is highly affected by the communication topology and drastically reduces when the information of the leader is sent to all followers. Finally, numerical results demonstrate the ability of the proposed methodology to impose the platoon control objective for the BPF and BPLF topology under random single packet drop

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

Extended cooperative adaptive cruise control

In this paper the Cooperative Adaptive Cruise Control strategy for vehicles platooning is extended to the case when each vehicle can communicate with a subset of vehicles in the fleet. The control objective is to guarantee that the fleet moves forward with a given spacing policy at the leader velocity. To this aim each vehicle decides its control action using information from all neighboring vehicles through wireless communication. In so doing, a network of dynamical systems is formed, and it is shown that achieving platooning is equivalent to find a control algorithm so that the resulting network is asymptotically stable. A network protocol able to deal with heterogeneous time-varying communication delays is then proposed to solve the problem. A consistent proof of stability of the closed-loop system is provided and numerical results confirm the effectiveness of the approach and its robustness with respect to variations of the leader velocity, as well as to generic topologies of the underlying network emerging from the communication features

An Extended Cooperative Adaptive Cruise Control (CACC) Algorithm for Efficient Energy Consumption & Traffic Density Formulation

Electric transportation, one of the most promising technologies of the century, can contribute to a greener environment as it is emission-free and sustainable. Although this technology promises a clean transportation style, it also has some drawbacks. One of the most significant one is cruising range, which needs to be addressed sustainably. The most eco-friendly solution is decreasing energy consumption by addressing driving behaviour. This can be achieved by taking the advantage of implementing an advancing vehicle automation technology which controls vehicles using a driver-assistance system such as Eco-Cruise Control (Eco-CC). Variety of systems already exist in the literature and a little known advanced version Eco-Adaptive Cruise Control (Eco-ACC) systems are developed as well. The next step of the vehicle automation is vehicle cooperation and information sharing, so-called Cooperative Adaptive Cruise Control (CACC). It is already developed and tested by various researcher. However, the largest deal of existing studies focus on assessing the performance in terms of safety, possible contributions to the energy consumption is not taken into account. This study covers the extension of Cooperative Adaptive Cruise Control systems while aiming to provide an energy efficient extended control algorithm to increase the energy efficiency and battery usage for electric vehicles. An energy efficient control algorithm is aimed to be derived to decrease the consumption of the vehicle. Cruising velocities and vehicle positions are received from the leading vehicles and accordingly traction force is adjusted to achieve efficient energy consumption. By providing vehicle to vehicle (V2V) communication tighter spacing gaps, lower time headway, are aimed to obtain while traffic disturbances are damped, whereas in the cases ACC applications amplify the disturbance. Traffic density formula is introduced by using V2V communication which might be useful for ADAS and ITS framework. As a result, increase in traffic stability, density, and reduction in the total energy consumption is expected. Moreover, possible reductions in air drag with tighter spacing gaps may lead reduction in energy consumption. For the energy calculations and the validation of the proposed method, vehicle dynamics and energy consumption of an electric car is formulated, which has completely different characteristics and limitations than combustion engine cars. Hence the study aims to provide additional understanding of behaviour of a fleet of CACC-equipped electric vehicles. Even though the proposed control algorithm is developed for Electric Vehicles, it can be extended to other vehicle types based on their energy consumption characteristics and vehicle dynamics