Merging on highways poses challenges due to limited visibility, fluctuating speeds, and possibly erratic human behavior. Since human drivers and autonomous vehicles might cohabit in mixed traffic, researchers investigated ways to alleviate the risk in these situations. They designed connected autonomous vehicles that embed empathy into their decision-making, enabling them to maneuver altruistically through coordination to assist merging vehicles. However, the percentage of connected autonomous vehicles will be too small in the early phases of mixed traffic to carry out such coordination and yield the expected safety impact. Therefore, we propose Archicool, an autonomous vehicle model performing altruistic maneuvers without any communication device. Based on multi-agent reinforcement learning and social value orientation, Archicool improves safety for merging vehicles while minimizing disturbances to traffic flow. In medium-traffic density, selective empathy reduces the risk perceived by merging vehicles by 16%.

The implementation of real-time gait mode detection is paramount in effectively providing tailored support for individuals utilizing actuated ankle-foot orthoses (AAFOs), thereby enhancing their walking capabilities and overall mobility. However, existing systems often rely on multiple sensors and struggle with accurate and prompt detection of gait transitions, especially in varied and challenging environments. This study aims to develop a novel real-time gait mode detection system that accurately identifies five essential daily living gait modes, namely level walking, ramp ascent/descent, and stair ascent/descent using only two foot-mounted inertial measurement units (IMUs). By using a long short-term memory (LSTM)-based algorithm trained on data collected from ten healthy subjects, the system extracts six kinematic features to predict gait modes with high accuracy. The proposed method integrates this detection system with a task-oriented control strategy to adapt the control of the AAFO based on the identified gait modes. The real-time experiments involving three healthy participants demonstrated robust gait mode detection, achieving an average estimation accuracy of$98 \pm 1$% across the five gait modes, even with the application of assistive torque. In cases mimicking abnormal gait, the system maintained an accuracy of$93 \pm 3$%. Additionally, each transition delay between gait modes was analyzed, showing that gait mode detection can occur between the transitions of the leading and trailing foot. The results of the control strategy showed a reduction in muscle activation of the dorsiflexor and plantarflexor muscles as measured by EMG, as well as improved tracking performance during the swing phase. Gait mode detection robustness was further evaluated by including walking with obstacles and changes in environmental dimensions.

Powered-Two-Wheelers (PTW) riders’ fatalities are prevalent on bends outside built-up areas due to the complexity and instability of their vehicles: countermeasures require a better understanding of the rider-PTW interaction. Analysing riding data is effective but becomes challenging when using extensive datasets; segmenting the riding data would help identify events of interest, isolate specific manoeuvres and describe the riding session. Manual segmentation would be time-consuming and subjective; automation would be beneficial. This work proposed an automatic, unsupervised tool for segmenting and clustering signals acquired during a riding session for studying motorcycle lateral dynamics in-depth. The method only requires measuring the motorcycle roll angle. An expert rider completed a closed route using an instrumented motorcycle; the algorithm divided the time series into segments categorised into clusters relative to specific riding conditions. Analysing the segmented trial revealed the effectiveness and usefulness of the approach. Then, a corner entry manoeuvre was investigated in-depth to observe each segment’s properties. The method associated each riding primitive to a cluster and described each manoeuvre through the segments’ succession. The clusters were unambiguous and easy to interpret thanks to their dynamics-based nature and minimal overlap. The algorithm identified the differences between the three corner entry manoeuvres in the trial. The segmentation simplified the in-depth corner entry analysis and allowed early detection of the manoeuvre start. The proposed tool can aid research on motorcycle dynamics, PTW-rider interaction, and riding preferences in bends. The segmented time series could be employed for rider training and pre-crash fall dynamics reconstruction.

Understanding driver-vehicle interactions remains a challenge, particularly in the case of cornering. This is particularly the case for powered two-wheeler vehicle (PTWs) users, perhaps because PTW drivers play a greater role in controlling the stability of their vehicles than do four-wheeled vehicle drivers. This difficulty stems from the variety of practices of this population of road users when entering, controlling their path, and exiting turns. Thus, observing the evolution of rider behavior during a cornering maneuver is an essential step in identifying road environment features that are risk factors for this category of road users. The real data set used for the experiments reported here was collected in the framework of the VIROLO++ collaborative project to improve the knowledge of the real practices of PTWs drivers, particularly during cornering. The in-depth analysis of these data in order to better understand motorcyclists’ behavior can therefore be considered a challenge. For this purpose, a two-step methodology was applied: (1) a data segmentation and feature extraction step in which the multidimensional time series of roll angle and roll velocity data were segmented using a multiple regression hidden logistic process (MRHLP), and (2) a clustering step in which the two detected segments characterizing the curve entry were assigned to different clusters regarding the curve initiation and the control actions set up during the different phases of the turning maneuver based on the hierarchical clustering algorithm. The results obtained show the effectiveness of the proposed methodology.

The powered two-wheelers (PTW) are among the most vulnerable transport users.It is crucial to identify the appropriate action that should be undertaken during a specific situation to reduce the risk.In this article, the aim is to improve the current state of the art in identification of riding patterns through neural network architectures and to explain how a decision is made by a model which is considered as a black box.In this regard, a new visualization tool specific to time series is suggested to help identify the most influential factors and hopefully to develop appropriate risk mitigation strategies.

In this work, we investigate the accuracy of attitude estimation solutions based on inertial/magnetic sensors in the context of motorcycle trajectory reconstruction. We propose a comparative study and analysis of a number of quaternion based attitude-filtering methods. We conduct an experimental study using a multi-sensor platform with a precise ground truth obtained with a GPS RTK. We quantify the error in the motorcycle trajectory estimation obtained with each method and we discuss the obtained results.

Driving errors are considered to be the greatest contributory cause in all road accidents and an important contributory cause of most fatal accidents. This is particularly the case for the users of powered two-wheeled vehicles (PTWs), perhaps because PTW riders play a greater role in the control of their vehicles' stability than four-wheeled vehicle drivers. Thus, observing and analyzing the evolution of riders' behavior in a real-life context is an important step in the identification of the road environment characteristics that constitute a risk factor for PTW riders. A relevant research issue in naturalistic studies is related to the detection and identification of critical riding events from among the vast amount of data recorded during the experiment. In this paper, two approaches were used to automatically detect such critical riding events. First, we formalized this problem in terms of detecting changes in the mean and variance of the signals generated by the acceleration and angular velocity sensors. For this purpose, two steps were performed: (1) a data segmentation and feature extraction step in which the multidimensional time series of accelerometer and angular velocity data were segmented and modeled using a Gaussian mixture model with quadratic logistic proportions; and (2) a classification step in which each detected segment was assigned to the appropriate riding sequence, whether “naturalistic” or “critical” (i.e., fall or near fall), using the k-nearest neighbor algorithm. The second approach was based on online fall detection. This methodology used control charts (a multivariate cumulative sum), an approach that has been traditionally employed for sequential detection. These two algorithms were applied to a database composed of data from a real-life driving experiment. The obtained results show the effectiveness of both methodologies.

In this paper, a machine-learning framework is used for riding pattern recognition. The problem is formulated as a classification task to identify the class of riding patterns using data collected from 3-D accelerometer/gyroscope sensors mounted on motorcycles. These measurements constitute an experimental database used to analyze powered two-wheeler rider behavior. Several well-known machine-learning techniques are investigated, including the Gaussian mixture models, the k-nearest neighbor model, the support vector machines, the random forests, and the hidden Markov models (HMMs), for both discrete and continuous cases. Additionally, an approach for sensor selection is proposed to identify the significant measurements for improved riding pattern recognition. The experimental study, performed on a real data set, shows the effectiveness of the proposed methodology and the effectiveness of the HMM approach in riding pattern recognition. These results encourage the development of these methodologies in the context of naturalistic riding studies.

Fog is a local meteorological phenomena which drastically reduces the visibility range. Fog detection and visibility range estimation are critical tasks for road operators who need to warn the drivers and advise them on speed reductions. To achieve this task, fixed sensors are quite accurate but they have a reduced spatial cover. Mobile sensors are less accurate, but they have a good spatial cover. Based on the combination of roadside sensors and in-vehicle devices (sensors or fog lamps), a data fusion framework is presented aiming at taking the advantages of both fixed and mobile sensors for the extensive detection and estimation of the fog density. The proposed solution is implemented by means of a local dynamic map fed by vehicle to infrastructure (V2I) communication, which gives a coherent view of the road environment.