Electric and hybrid propulsion systems for articulated vehicles have been gaining increased attention, with the aim to decrease exhaust particle emissions. However, the more environmentally-friendly electric or hybrid articulated vehicles are expected to have increased nonexhaust pollution-related sources because of their significantly increased mass compared with conventional vehicles. One of the main sources of nonexhaust pollution is tire wear, which could potentially cancel the benefits of removing the exhaust through electrification. Tire wear is mainly affected by internal (tire structure and shape) and external (suspension configuration, speed, road surface, etc.) factors. This work focuses on suspension systems and, more specifically, on the ability of active and semiactive suspensions to decrease tire wear in an articulated vehicle. In this direction, an articulated vehicle model that incorporates the tread in its modeling is built to study tire wear during cornering over a class C road. A novel active suspension design based on the H approach is suggested in this work and is compared with passive, semiactive, and other active suspension systems. The suspension systems are also compared mainly with regard to tire wear levels but also with other vehicle performance aspects (i.e., comfort and road holding). The Hop active suspension design is the most effective in decreasing tire wear, with decreases of about 8% to 11%, but without neglecting the rest of the objectives.

Remote driving plays an essential role in coordinating automated vehicles in some challenging situations. Due to the changed driving environment, the experiences and behaviors of remote drivers would undergo some changes compared to conventional drivers. To study this, a continuous real-life and remote driving experiment is conducted under different driving conditions. In addition, the effect of steering force feedback (SFF) on the driving experience is also investigated. In order to achieve this, three types of SFF modes are compared. According to the results, no SFF significantly worsens the driving experience in both remote and real-life driving. Additionally, less force and returnability on steering wheel are needed in remote driving, and the steering force amplitude appears to influence the steering velocity of remote drivers. Furthermore, there is an increase in lane following deviation during remote driving. Remote drivers are also prone to driving at lower speeds and have a higher steering reversal rate. They also give larger steering angle inputs when crossing the cones in a slalom manoeuvre and cause the car to experience larger lateral acceleration. These findings provide indications on how to design SFF and how driving behavior and experience change in remote driving.

Effective emission control technologies and eco-friendly propulsion systems have been developed to decrease exhaust particle emissions. However, more work must be conducted on non-exhaust traffic-related sources such as tyre wear. The advent of automated vehicles (AVs) enables researchers and automotive manufacturers to consider ways to further decrease tyre wear, as vehicles will be controlled by the system rather than by the driver. In this direction, this work presents the formulation of an optimal control problem for the trajectory optimisation of automated articulated vehicles for tyre wear minimisation. The optimum velocity profile is sought for a predefined road path from a specific starting point to a final one to minimise tyre wear in fixed time cases. Specific boundaries and constraints are applied to the problem to ensure the vehicle’s stability and the feasibility of the solution. According to the results, a small increase in the journey time leads to a significant decrease in the mass loss due to tyre wear. The employment of articulated vehicles with low powertrain capabilities leads to greater tyre wear, while excessive increases in powertrain capabilities are not required. The conclusions pave the way for AV researchers and manufacturers to consider tyre wear in their control modules and come closer to the zero-emission goal.

Autonomous vehicles (AVs) are expected to have a great impact on mobility by decreasing commute time and vehicle fuel consumption and increasing safety significantly. However, there are still issues that can jeopardize their wide impact and their acceptance by the public. One of the main limitations is motion sickness (MS). Hence, the last year's research is focusing on improving motion comfort within AVs. On one hand, users are expected to perceive AVs driving style as more aggressive, as it might result in excessive head and body motion. Therefore, speed reduction should be considered as a countermeasure of MS mitigation. On the other hand, the excessive reduction of speed can have a negative impact on traffic. At the same time, the user's dissatisfaction, i.e., acceptance and subjective comfort, will increase due to a longer journey time. Therefore, additional approaches to mitigating MS should be considered without affecting journey time, such as vehicle and seat suspension designs. In this direction, this article investigates a novel active seat suspension (ActiveK) that operates according to K-seat. The K-seat is a novel passive isolator with negative stiffness (NS) elements proven to enhance comfort, but has difficulties in design, which can be overcome with ActiveK. The ActiveK-seat is benchmarked against a passive seat model, a semi-active model with a continuously controllable electromagnetic damper (EMD), and a simple active model. The seat models are not only compared with regard to comfort but also for their ability to mitigate MS while the vehicle is driving on a real road path with a Class C road roughness. The results are very promising and show up to a 70% and 25% decrease in metrics for discomfort and MS incidence, respectively, compared to the rest of the seat model.

This article proposes a disturbance observer-based event-triggered H ∞ controller for a semi-active seat suspension that equips an advanced electromagnetic damper (EMD) system. Automated driving is one of the leading technologies of the automotive industry. However, automated vehicles (AVs) may increase the incidence of motion sickness (MS) and deteriorate motion comfort. This article investigates a semi-active seat suspension system and an advanced controller to improve the motion comfort of AVs. The disturbance force of the seat suspension has considerable influence on the system dynamic, and applying a constant model to describe the real-time disturbance force is unreliable. Therefore, a disturbance observer is designed to estimate the seat suspension disturbance force, and it is used to compensate the controller. The Bouc-Wen model is selected to compare with the disturbance observer and validate its effectiveness. Then an event-triggered H ∞ controller combined with the disturbance observer is proposed. The performance of the proposed controller is validated with experiments. Experimental results show that the proposed event-triggered H ∞ controller not only can save the communication resources of data transmission but also can work as a filter to increase motion comfort.

This paper investigates the fundamentals of motion planning for minimizing motion sickness in transportation systems of higher automation levels. The optimum velocity profile is sought for a predefined road path from a specific starting point to a final one within specific and given boundaries and constraints in order to minimize the motion sickness and the journey time. An empirical approach based on British standard is used to evaluate motion sickness. The trade-off between minimizing motion sickness and journey time is investigated through multi-objective optimization by altering the weighting factors. The correlation between sickness and journey time is represented as a Pareto front because of their conflicting relation. The compromise between the two components is quantified along the curve, while the severity of the sickness is determined using frequency analysis. In addition, three case studies are developed to investigate the effect of driving style, vehicle speed, and road width, which can be considered among the main factors affecting motion sickness. According to the results, the driving style has higher impact on both motion sickness and journey time compared to the vehicle speed and the road width. The benefit of higher vehicle speed gives shorter journey time while maintaining relatively lower illness rating compared with lower vehicle speed. The effect of the road width is negligible on both sickness and journey time when travelling on a longer road. The results pave the path for the development of vehicular technologies to implement for real-world driving from the outcomes of this paper.

The evolution of mobility is led by automated vehicles (AVs), as they are expected to decrease commute time and vehicle fuel consumption as well as significantly increase safety. One of the main limitations they face is motion sickness (MS), which could jeopardise AVs acceptance by the society. On one hand, AVs driving style is expected to be perceived more aggressive by AV users, which will cause more head and body motion. Hence, the control of the velocity and its minimisation are an efficient countermeasure of motion sickness mitigation in AVs. On the other hand, the excessive reduction of the velocity can significantly affect user’s dissatisfaction due to longer journey time. Therefore, additional approaches of mitigating MS have to be considered without affecting journey time. In this direction, this paper proposes an active integrated seat suspension for both longitudinal and vertical isolation to minimise MS. The model is compared with a conventional passive seat design for vertical isolation only, and a passive integrated seat design. All the seat models are excited by vehicle responses obtained from IPG/CarMaker when a vehicle is driven over a real countryside road with Class B road roughness. The results illustrate more than 50% improvement in comfort and 20% more MS mitigation compared to the conventional passive seat.

The allowable range of speed that a vehicle can tolerate in a constant radius turn is crucial for the development of smart assistance systems. Although the development of advanced system observers has been grown since early days of its introduction, extensive study is required in monitoring the vehicle’s behaviour in the conditions such as variation of vehicle dynamic parameters and terrain type. Autonomous vehicles will fail to judge the parameter of the road cornering due to the safety constraints of the vehicle. Thus, the primary concern of this paper is to study the vehicle’s behaviour for different curvature profiles. A real-time simulation for a typical Sedan is presented to test a constant roundabout turning with a radius of 50 m for this measure. In prior to that, a detailed analysis on the vehicle stability and handling responses are discussed. The vehicle is found to be traveling in a stable region at a speed from 10 to 74 km/h. The vehicle enters a critical area when speed is more than 74 km/h. Therefore, that the allowable range of speed that the vehicle can travel in a 50 m radius turn lies between 10 to 74 km/h. The stability is evaluated by two criterions which are the yaw rate and sideslip angle. 

Automated vehicles are expected to push towards the evolution of the mobility environment in the near future by increasing vehicle stability and decreasing commute time and vehicle fuel consumption. One of the main limitations they face is motion sickness (MS), which can put their wide impact at risk, as well as their acceptance by the public. In this direction, this paper presents the application of motion planning in order to minimise motion sickness in automated vehicles. Thus, an optimal control problem is formulated through which we seek the optimum velocity profile for a predefined road path for multiple fixed journey time (JT) solutions. In this way, a Pareto Front will be generated for the conflicting objectives of MS and JT. Despite the importance of optimising both of these, the optimum velocity profile should be selected after taking into consideration additional objectives. Therefore, as the optimal control is focused on the MS minimisation, a sorting algorithm is applied to seek the optimum solution among the pareto alternatives of the fixed time solutions. The aim is that this solution will correspond to the best velocity profile that also ensures the optimum compromise between motion comfort, safety and driving behaviour, energy efficiency, journey time and riding confidence.