Motion sickness is very common in road transport. To guarantee ride comfort and user experience, there is an urgent need for effective solutions to motion sickness mitigation in semi- and fully-automated vehicles. Considering both effectiveness and user-friendliness, a vibration cue system is proposed to inform passengers of the upcoming vehicle movement through tactile stimulation. By integrating the motion planning results from automated driving algorithms, the vibration cueing timing and patterns are optimised with the theory of motion anticipation. Using a cushion-based prototype of a vibration cue system, 20 participants were invited to evaluate this solution in two conditions of driving simulator experiments. Results show that the proposed vibration cue system could also help participants to comprehend the cues and to generate motion anticipation. The participants’ motion sickness degrees were significantly lowered. This research may serve as one foundation for detailed system development in practical applications. Practitioner Summary: In automated vehicles, passengers engaging in non-driving tasks are apt to severe motion sickness. A vibration cue system and cueing strategy are proposed and optimised to inform passengers of the upcoming vehicle movement. Simulator experiments of 20 participants proved its effectiveness in promoting motion anticipation and reducing motion sickness. Graphical Abstract

For autonomous driving, it is important to develop safe and efficient decision algorithms to handle multi-vehicle interactions. Game theory is suitable to manage the interactive driving decision modelling, however, common approaches of multi-player game formulation is computationally complex for dynamic and intense interactions. The main contributions of this work are two-fold: 1) a global sorting-local gaming framework, namely GLOSO-LOGA, is proposed to solve the intersection interaction problem for autonomous driving, which can comprehensively consider the advantages of multi-vehicle collaboration and single-vehicle intelligence approaches; 2) an interaction disturbance function is used to quantify the impact of indirect interactions on ego vehicle. To validate the algorithm performances, corner case simulations and human-in-the-loop simulator experiments are carried out, in which a four-armed intersection scenario with various urgent and challenging interaction conditions is used. Compared to a traditional approach that decomposes a multi-vehicle game into multiple two-vehicle games, the proposed algorithm can improve both safety and traffic efficiency in intensively interactive driving scenarios.

Unsignalized intersection driving is challenging for automated vehicles. For safe and efficient performances, the diverse and dynamic behaviors of interacting vehicles should be considered. Based on a game-theoretic framework, a human-like payoff design methodology is proposed for the automated decision at unsignalized intersections. Prospect Theory is introduced to map the objective collision risk to the subjective driver payoffs, and the driving style can be quantified as a tradeoff between safety and speed. To account for the dynamics of interaction, a probabilistic model is further introduced to describe the acceleration tendency of drivers. Simulation results show that the proposed decision algorithm can describe the dynamic process of two-vehicle interaction in limit cases. Statistics of uniformly-sampled cases simulation indicate that the success rate of safe interaction reaches 98%, while the speed efficiency can also be guaranteed. The proposed approach is further applied and validated in four-vehicle interaction scenarios at a four-arm intersection.

The connected automated vehicles (CAVs) are envisioned to be implemented most likely on electric vehicles, while traditional fuel-powered manually-driven vehicles (MVs) would probably still dominate the automobile market in the next decade. In this context, this paper addresses urban eco-driving of CAVs in mixed traffic and heterogeneous power conditions. The paper aims to develop a practical and deployable eco-driving strategy for CAVs in mixed traffic flow of CAVs and MVs under realistic and complex traffic conditions. Several typical eco-driving scenarios were studied in detail. In a nutshell, the eco-driving strategy for each CAV was determined by solving a typical two-point boundary value problem with minimum electric energy consumption in urban traffic conditions with small market penetration rates (MPRs) of CAVs. A rolling-horizon scheme was applied to implement the eco-driving strategy to handle uncertain/unpredictable disturbances of preceding MVs and the interference of junction queues to the eco-driving maneuvers of CAVs. The paper also studied how eco-driving for electrified CAVs would affect MVs’ fuel consumptions. Simulation studies were carried out on urban arterial roads of multiple signalized intersections in various scenarios of demand and MPR to verify the energy savings effect of the proposed eco-driving strategy. The results showed that via eco-driving electrified CAVs each had a potential of reducing energy consumption by 40%-61%, meanwhile leading to 5%-34% fuel savings on average for each following MV. Further issues concerning the energy saving mechanism of electrified CAVs, impacts of MVs cut-in from adjacent lanes, and passenger comfort were also examined.

Overtaking on two-lane two-way (TLTW) highways is often associated with a high risk of crashing. However, existing models of TLTW overtaking decision, either mechanism- or learning-based, cannot handle well the dynamic coupling among the interacting drivers. For accurate overtaking modelling, it is crucial to consider the uncertainties of interacting vehicle behaviours, especially their driving styles. To address these needs, we propose a TLTW overtaking decision model using a level-k game theoretic framework, which can consider the mutual influences between the ego and oncoming vehicles of different driving styles. A dataset is built based on the TLTW overtaking experiments with two instrumented vehicles, then PCA and k-means clustering are used to classify three driving styles, i.e. aggressive, normal and conservative. By comparing the model predictions with the experiment data, the statistics and case studies show that the proposed model with driving style awareness can accurately describe driver decisions in TLTW overtaking.