Theme issue on connected and automated road vehicles

In recent years, Connected and Automated Vehicles (CAVs) have become a major focus of research and development efforts in the automotive industry. CAVs are expected to significantly impact the transportation, energy and land use and the broader economy and society.

CAVs have also re-invigorated the field of vehicle dynamics and control, and inspired many contemporary directions for research. The cross disciplinary focus in which the treatment of vehicle dynamics and control is integrated with developments in perception, artificial intelligence based decision making, motion planning, energy management, wireless communications, cybersecurity, etc. has added much excitement to the field, and attracted many researchers from other domains. CAVs have also promoted a paradigm shift with real interest emerging in the use of advanced (by automotive industry standards) methods, such as model predictive control and reinforcement learning, in production.

Consequently, a Theme Issue which is focused on the ongoing research on CAVs is both relevant and timely to the Vehicle Systems Dynamics journal. The papers in this Theme Issue come from several leading research groups in automotive control worldwide; the papers correspond to a subset of invited talks given at 3rd IAVSD Workshop on Dynamics of Road Vehicles held on 28–30 April 2019 in Ann Arbor, Michigan, USA. A brief description of the contributions follows.

The paper by T. Ersal, I. Kolmanovsky, N. Masoud, N. Ozay, J. Scruggs, R. Vasudevan and G. Orosz titled ‘Connected and Automated Road Vehicles: State of the Art and Future Challenges’ provides a general overview of the field. Recent approaches to modelling the dynamics and model based control design for automated vehicles are summarised, and new methods for safety verification of these controllers are highlighted. The advantages of vehicle automation and connectivity in powertrain control and optimisation are quantified and the positive impacts of CAVs on traffic dynamics are emphasised. Finally, the long-term societal effects of CAV deployments are discussed and future opportunities in the field are pointed out.

The paper by K. Berntorp, R. Quirynen, T. Uno and S. Di Cairano titled ‘Trajectory Tracking for Autonomous Vehicles on Varying Road Surfaces’ describes the development and implementation of an adaptive nonlinear model predictive controller for vehicle dynamics. The tire stiffness is estimated under normal driving conditions (when slip is small) and this estimate is used to inform the entire tire force curve based on the model library. The latter is used for model predictive control during driving at tire-road adhesion limits. Feasibility of real time implementation is demonstrated, and preliminary experimental results are included.

The paper by R. Hult, M. Zanon, S. Gros, H. Wymersch and P. Falcone titled ‘Optimization-based Coordination of Connected, Automated Vehicles at Intersections’ proposes a two stage procedure for controlling traffic flow through intersections. In the first stage vehicle crossing order is assigned by solving a mixed integer quadratic programme; then an optimal control problem is solved using nonlinear programming. The proposed two stage optimisation is implemented over the receding horizon and is shown to reduce energy consumption and travel time/traffic capacity while ensuring no collisions in multi-vehicle intersection crossing scenarios.

The paper by G. Na, G. Park, V. Turri, K. Johansson, H. Shim and Y. Eun titled ‘Disturbance Observer Approach for Fuel-efficient Heavy-duty Vehicle Platooning’ focuses on incorporating the effects of road slope into the controllers used in truck platooning. In order to avoid the use of road-slope datasets a disturbance observer is constructed to estimate the road slope and to enable the platoon to calculate the optimal velocity profile without using actual road slope data. It is shown that this approach achieves both fuel savings and robust velocity tracking performance. Simulations of various scenarios are used to demonstrate the efficacy of the proposed method while utilising actual road slope data of a Swedish highway.

The paper by S.-E. Li, H. Che, R. Li, Z. Liu, Z. Wang and Z. Xin titled ‘Predictive Lateral Control to Stabilize Highly Automated Vehicles at Tire-Road Friction Limits’ adopts a linear Model Predictive Control (MPC) approach to control an automated vehicle at the tire-road adhesion limits while tracking a desired trajectory. Steps to successfully implement a linear MPC approach for control of the vehicle in the nonlinear regime are described and simulation results are reported to verify the proposed approach including its robustness to inaccuracies in road friction estimation.

The paper by L. Beaver, B. Chalaki, A. Mahbub, L. Zhao, R. Zayas and A. Malikopoulos titled ‘Demonstration of a Time-efficient Mobility System Using a Scaled Smart City’ presents the implementation of a decentralised control framework in a scaled-city using robotic CAVs that are able to replicate real-world traffic scenarios in a controlled environment. Control algorithms of CAVs are validated in specific transportation situations at intersections, merging roadways, and roundabouts. Energy optimal trajectories are tested and the improvements in travel time are quantified experimentally.

The paper by C. Huang, R. Salehi, T. Ersal and A. Stefanopoulou titled ‘An Energy and Emissions Conscious Adaptive Cruise Controller for a Connected Automated Diesel Truck‘ develops a model predictive controller (MPC) for vehicle following with the emphasis on energy and emissions optimisation for a diesel truck with a selective catalytic reduction (SCR) system. The details of modelling, controller design and its implementation in simulations are described. The potential for fuel economy improvement and emissions reduction is demonstrated while deficiencies in terms of degraded emissions with purely energy efficiency focused optimisation are highlighted.

The paper by O. Hassanain, M. Alirezaei, J. Ploeg and N. van de Wouw titled ‘String-Stable Automated Steering in Cooperative Driving Applications’ proposes a design methodology for cooperative lateral controllers of vehicle platoons. The dynamics of the vehicles are described using the dynamic bicycle model and the proposed controller is designed using the H_∞ control framework. It is shown that the resulting controller can achieve path-following, where the path is induced by the preceding vehicle in the platoon, as well as lateral string stability. The performance of the system is evaluated by utilising frequency domain analysis and by simulating lane-change maneuvers.

We hope that the readers will find this Theme Issue interesting and valuable and that the new insights and results will be useful to researchers and engineers working on Connected and Automated Road Vehicles. We thank the authors for their contributions to this issue and the speakers who shared their results by giving enlightening presentations at the workshop. We also would like to thank the anonymous reviewers. Finally, we would like to acknowledge and thank the International Association for Vehicle System Dynamics (IAVSD) board for their support. Our special thanks go to Professor Timothy Gordon, Professor Manfred Plöchl and Professor Mehdi Ahmadian who made the workshop and this Theme Issue possible.