Contents With the promise of safer transportation, autonomous vehicles (AVs) are expected to increase road safety by 94%. However, accidents still occur involving AVs, which attracts increasing attention from academia and industry. To achieve the maximum potential for improved transportation safety, efforts are required to seek solutions from fundamental theory, key technology, guarantee frameworks, and so on. This special section aims to provide a platform for researchers and engineers from academia and industry and policy makers to present their latest research findings and engineering experiences in developing and applying novel technologies to improve and address transportation safety. The topics cover safety of the intended functionality for intelligent vehicles, risk assessment, control of AVs and vehicle platoons, and so on.
The continuous validation of the operational design domain (ODD) of an automated driving system (ADS) and the augmentation of its adequate safe operating space employing iterative engineering seems to be a prudent approach to improve the safety performance and integrity of AVs. “Acclimatizing the Operational Design Domain for Autonomous Driving Systems” by Chen Sun et al., the University of Waterloo, proposed an ODD acclimatization framework based on the driving scenario and environment models. This article provides promising directions that display great potential for future work on ODD monitoring and applications in iterative development for ADSs.
“Distributed Stochastic Model Predictive Control for Heterogeneous Vehicle Platoons Subject to Modeling Uncertainties,” by Zhiyang Ju et al., Beihang University, discusses the design of a distributed stochastic model predictive control (DSMPC) strategy for vehicle platoons, with each vehicle subject to uncertainties, and proposes a modified constraint tightening policy and a control input update policy to address the DSMPC problem. This article analyzes and theoretically proves the effectiveness of the proposed DSMPC method for the platoon system.
“Jam-Absorption Driving Strategy for Improving Safety Near Oscillations in a Connected Vehicle Environment Considering Consequential Jams,” by Shunchao Wang et al., Southeast University, proposes an optimal jam-absorption driving strategy that not only prevents the original traffic oscillation but also considers the avoidance of a secondary jam. The proposed strategy showed a better performance than some previous ones under the same traffic conditions, demonstrating the potential to effectively improve the safety situation on the freeway.
“Human–Machine Adaptive Shared Control for Safe Driving Under Automation Degradation,” by Chao Huang et al., Nanyang Technological University, proposes a human–machine adaptive shared control method for AVs under automation performance degradation, which consists of a novel risk assessment module and an adaptive control authority allocation module. This article presents experimental validation under different driving scenarios, and the method displays great potential to ensure the safety of automated driving.
The traditional edge-side (vehicle-side) dynamics control system strictly limits the performance improvement of a multifunctional unmanned ground vehicle (UGV) in various applications. “A Cloud Big-Data-Driven Dynamics Control Approach for Unmanned Ground Vehicles for Safety Improving,” by Xu Jiang et al., Beijing Institute of Technology, proposes a future intelligent transportations system (ITS) with multifunctional UGVs and a cloud-edge combined big-data driven dynamics control approach for UGVs to improve the safety. This article presents the potential of the combination of cloud-side and edge-side controllers to improve the path tracking performance and safety.
Dynamic couplings, various disturbances, and uncertainties have posed a big challenge to trajectory tracking control. “Adaptive Finite-Time Trajectory Tracking Control of Autonomous Vehicles That Experience Disturbances and Actuator Saturation,” by Hongbo Gao et al., the University of Science and Technology of China, presents a finite-time tracking control scheme for AVs that uses a fuzzy logic system to cope with the lumped disturbance and an auxiliary system to tackle the actuator saturation. The method demonstrates the ability to guarantee finite-time error convergence, chattering elimination, and strong robustness.
The publication of this special section would not be achieved without the meticulous work and joint efforts of the authors, reviewers, editors, and the editorial office of IEEE Intelligent Transportation Systems Magazine, and we would like to express gratitude to all of them for their hard work and support. We are also indebted to the support from Tsinghua University, Nanyang Technological University, Chongqing University, the Chinese Academy of Sciences, Indiana University–Purdue University Indianapolis, the University of Waterloo and Delft University of Technology. It is our hope that this special section can inspire scientific ideas and promote research in the field of safety of automated driving in an ITS.