Motion sickness (MS) has long been a common complaint in road transportation. However, in the era of driving automation, MS has become an increasingly significant issue. The future intelligent vehicle is envisioned as a mobile space for work or entertainment, but unfortunately passengers' engagement in non-driving tasks may exacerbate MS. Finding effective MS countermeasures is crucial to ensure a pleasant passenger experience. Nevertheless, due to the complex mechanism of MS, there are numerous challenges in mitigating it, hindering the development of practical countermeasures. To address this, we first review two prevalent theories explaining the mechanism of MS. Subsequently, this paper provides a summary of current subjective and objective approaches for quantifying motion sickness levels. Then, it surveys existing methods for alleviating MS, including passenger adjustment, intelligent vehicle solutions, and motion cues of various modalities. Furthermore, we outline the limitations and remaining challenges of current research and highlight novel opportunities in the context of intelligent vehicles. Finally, we propose an integrated framework for alleviating MS. The findings of this review will enhance our understanding of carsickness and offer valuable insights for future research and practice in MS mitigation within modern vehicles.

The prevalence of motion sickness among passengers using personal electronic devices, such as smartphones, during vehicle journeys has become a growing concern. This issue is expected to intensify with the increasing adoption of assistant or automated driving functions, which may lead to non-driving tasks (NDT) being performed by all on-board passengers, including the user in the "driver" seat during conditionally or fully automated driving modes. This trend presents challenges related to motion sickness, particularly in terms of specific performance requirements for non-driving tasks. In response to the need to alleviate passenger motion sickness, we have developed an easy-to-understand animation cue app that can be conveniently implemented on smartphones. The motion cue conveys information about vehicle accelerations, including their directions and magnitudes, using the metaphors of traffic signal colors and backward-moving lane lines, either in straight or curved lane driving. Following several rounds of improvements based on moving-base simulator and real car experiments, finally a successful cue design was found, which could significantly alleviate motion sickness of passengers while engaging in NDT, with minimal impact on their NDT performances. However, the study also revealed limitations to the sickness-alleviating capability of our motion cue design, including potential lack of universal acceptance among different users and reduced effectiveness in severely uncomfortable driving conditions. This work may provide valuable insights for further visual cue improvements that can contribute in future carsickness-proof vehicles.

Autonomous vehicle, though rapidly developing, still faces several challenges, one of which is motion sickness. It is necessary to mitigate motion sickness without sacrificing driving safety. Even if experiencing the same vibrational stimulus, people experience motion sickness differently. However, in the field of motion planning that considers motion sickness, current studies are mostly focusing on how to reduce the discomfort index in general ways, i.e. not considering for any specific passenger. Therefore, to exploit further potential of sickness mitigation, two important issues need to be solved, 1) how to predict the motion sickness level; 2) how to plan a sickness-less trajectory. To this end, firstly we construct a radial basis function neural network model to predict the motion sickness level of passenger, based on the extensive experimental data that we accumulated previously. Then, we integrate this passenger-specific model into trajectory planning in autonomous vehicles, which is formulated as an optimal control problem. Finally, two typical scenarios prone to evoking motion sickness have been simulated to validate the proposed algorithm, including the lane change while decelerating and the longitudinal accelerating scenarios. Results demonstrate that our algorithm can generate safe and comfortable trajectories that are customizable according to passenger's specific susceptibility to motion sickness. Particularly, for those highly susceptible to motion sickness, our algorithm can reduce their motion sickness degree by 21.55% and 15.68% in the two above scenarios, respectively, compared with the conventional planning algorithm based on polynomial.

Driving on bumpy roads with potholes or bulges may severely compromise vehicle ride comfort, while in extreme cases safety may be affected. Existing approaches mainly focus on suspension control to ease the discomfort by adjusting equiv-alent damping and stiffness or exerting active forces to ease the ride. In conventional approaches, these adjustments need to know the irregular input information of road ahead via camera or lidar sensors. In car following scenarios, it is difficult to directly get such information, since a pothole may be occluded by preceding vehicles. This work proposes an alternative way to alleviate passenger discomfort by proactively estimating the road irregularity based on preceding vehicle responses, and then by optimizing the timing of road irregularity excitation in speed planning, the weighted discomfort index is reduced. First, a Kalman filter with general knowledge of vehicle suspension dynamics is designed, which can output pothole distribution results according to the vertical responses of preceding vehicle body. Then, considering the sensitive frequency band of pas-senger, a motion planning algorithm is proposed to optimize the frequency-weighted acceleration when passing the road irregularity. Compared with a typical car following model IIDM-CAH, the proposed algorithm shows effective mitigation of discomfort caused by the road irregularities, while not sacrificing safety performances. This approach may be applied in automated vehicles, and may also work together with active suspension control to achieve better improvement in passenger comfort.

It is common for passengers to perform non-driving tasks such as reading or gaming on smart phones when riding, which may easily lead to car sickness. The underlying cause is a possible sensory conflict between the signals received by the passenger's visual processing system and those received by the vestibular system. To reduce the conflict and mitigate car sickness, we propose the MOSI APP, a smart phone application to present the vehicle motion as a visual cue. Four arrows on phone screen are used for cueing, while the direction and color-filling indicate the vehicle motion direction and magnitude, respectively. When using the smart phone for non-driving tasks, passengers can observe and understand motion cues to gain situation awareness through the MOSI APP. A total of 30 participants of passenger were recruited for a series of driving simulator experiments, and reports of their sickness level, non-driving task performances and subjective evaluation on our application were collected and analyzed. Results show that: 1) for all 26 participants with valid data, comparing to the case without motion cues, the MOSI APP could contribute to motion sickness mitigation (though with no significance, $$p=0.157$$ ), and it could delay the aggravation of moderate or severe sickness level (by 8 to 10 min in our experiment setting); 2) for the 15 participants who were more susceptible to motion sickness in the experiments, the APP could significantly ( $$p = 0.047$$ ) alleviate their sickness level, while their non-driving task performances were not notably affected. This study may provide useful insights in how to mitigate car sickness, especially in the era of ubiquitous computing and connectivity on smart devices.

Driving strategy in dynamic environment is crucial to the automated vehicle safety. In extremely emergency scenarios with unavoidable collision (UC), especially those with complex impact patterns, the potential crash risk should be well considered. This paper proposes a crash mitigation (CM) algorithm for UCs, which directly embeds a generalized crash severity index (CSI) model to vehicle-to-vehicle collisions of multiple impact patterns. The idea is that during the short time before a collision, the vehicle will actively adapt its position and poses to minimize the potential crash severity level after the collision. To this end, the generalized CSI model is introduced to estimate the potential crash severity of all sample paths, from which a crash-severity-optimal trajectory is obtained. To improve the inferring time efficiency of the planning module, a neural network is constructed and deployed to approximate the nonlinear severity model. The proposed algorithm is first validated through simulations of UC scenarios, including entry ramp merging, intersection crossing and downhill/uphill crossing. Then for the intersection crossing scenario, the algorithm is deployed to a real car and validated through digital-twin experiments. Results show that by combining the braking and steering interventions for better crash severity reduction, the proposed strategy can achieve better mitigation effects than commonly-used collision avoidance (CA) strategies. This reveals that a new mindset of comprehensive safety strategy should not focus only on CA, but also the last resort of CM if collision is unavoidable. Our work may contribute as a promising solution to the safety problem in emergency scenarios.

Motion planning in dynamic environment is crucial to the automated driving safety. In extremely emergency scenarios with unavoidable collisions, especially those with complex impact patterns, the potential crash risk should be well considered in motion planning. This paper proposes a motion planning algorithm for unavoidable collisions, which directly embeds a generalized crash severity index model to vehicle-to-vehicle collisions of multiple impact patterns. Firstly, the clothoid curve is used to sample the vehicle trajectory before collision, and a two-degree-of-freedom model is adopted to predict the vehicle poses corresponding to each sample path. Then, the crash severity index model is to estimate the potential crash severity of all sample paths. To improve the inferring time efficiency, a neural network is constructed and deployed to approximate the nonlinear severity model. Finally, the crash-severity-optimal trajectory is tracked through model predictive control method. Results show that by combining the braking and steering interventions for better crash severity reduction, the proposed strategy can achieve better mitigation effects than commonly-used collision-avoidance strategies. The deployment of real car experiment and sensitivity analysis demonstrate that the planning algorithm can guarantee real-time and reliably safe performances.

For automated driving, trajectory prediction of other surrounding vehicles is crucial to ego vehicle’s driving decision. This is especially important when automated vehicles, e.g. SAE level 3 vehicles or fully-automated robotaxis, share the open road with human-driven vehicles. One of the main research directions in this field is to adopt the methods of deep learning. We propose a TCN-MLP encoder-decoder framework that considers both the trajectory data of predicted vehicle and surrounding vehicles in the input. To handle more complex trajectory prediction, a driving intention recognition module is added to the model to identify the intentions in longitudinal and lateral directions. Based on the HighD dataset, we have tested the proposed model and its two variations for ablation experiment. The results show that our model can achieve more accurate trajectory prediction than the state-of-the-art approaches, and the prediction RMSE is reduced by about 33.3% on average. Our model may serve as a promising solution to vehicle trajectory prediction problems in highway scenes.