Where drivers are looking at during takeover: Implications for safe takeovers during conditionally automated driving.

OBJECTIVE: Safety has become one of the primary concerns of level 3 automated driving, especially during the takeover process. Since most studies have focused on impacts of various factors on takeover performance of drivers, there seems to be a gap between the causes of crashes and the desired means to mitigate their occurrence and consequences. Hence, the main objective of this study is to extract from crash data during takeovers drivers' patterns of gaze behaviors and maneuvers and then utilize them to extract some guidance on human-machine-interface design to enhance safety and acceptability of automated driving. METHODS: A study involving 27 subjects was conducted on a high-fidelity driving simulator with a Steward motion platform of six degrees of freedom. Each subject participated in 6 takeover scenarios with a lead time of 5 s and different duration of monitoring (DoM), with their maneuvers recorded by the system and eye gazes recorded by the Smart Eye Pro and Smart Recorder. Crash data collected during the takeover process were then utilized for the analysis. RESULTS: From 132 valid takeovers collected from 23 out of the 27 participants, 15 crashes were recorded. Based on which, five typical patterns of unsafe behaviors were recognized that may have caused the crashes, denoted as Type I to Type V, respectively. Besides, it appears that even if drivers were given more time to observe the surroundings, i.e., longer DoM, the number of crashes has not decreased as anticipated. Therefore, what is more important seems to be drivers' gaze behaviors and maneuvers shortly after TOR. CONCLUSIONS: For takeovers to be safe, good cooperations between drivers' gaze behaviors and maneuvers are essential. Overall, it seems that in emergent situations that require takeovers, some drivers have difficulty in allocating attentions reasonably, which appears to have less to do with the time left for drivers to observe the surroundings. While designing HMIs, we may as well consider providing enough information to guide drivers according to drivers' states and maneuvers at the time to improve safety of takeovers in emergent situations, and more importantly, to provide the information timely and effectively.

Driver behaviors assisted by different human machine interfaces to avoid rear-end collisions during level 2 automated driving.

OBJECTIVE: To practically apply level 2 automated driving in complex traffic conditions, it is necessary to prompt driver behaviors to prevent potential accidents in areas where manual interventions are frequently required. METHODS: A driving simulator experiment with 20 participants was conducted to evaluate the impact of different human machine interfaces (HMIs) on drivers' interventions in terms of braking to avoid rear-end collisions during level 2 automated driving when a motorcycle abruptly cut in near intersections. Two types of HMIs were tested: a static HMI that informed drivers about approaching intersections, and a sensor HMI that displayed real-time object recognition results. Each driver participated in five experimental conditions, which varied the presence or absence of the static and sensor HMIs during level 2 automated driving, with manual driving serving as the baseline condition. RESULTS: The maximum deceleration in terms of braking to avoid rear-end collisions was significantly larger when level 2 automated driving was used without any HMI, compared to that of manual driving. However, when the sensor HMI was applied together with the static HMI during level 2 automated driving, a comparable time to collision could be achieved with a significantly smaller deceleration, compared to that without any HMI. Drivers' eye-gaze behaviors revealed that no significant difference existed in the percentages of gaze to the road center area, indicating that they were not distracted by the HMIs. Finally, drivers' attention levels to surrounding traffic and feeling of safety were significantly higher when level 2 automated driving was used in combination with the static and sensor HMIs. CONCLUSIONS: The results demonstrated that the combination of static and sensor HMIs successfully aided drivers in ensuring driving safety with a significantly smaller deceleration to avoid rear-end collisions during level 2 automated driving. Furthermore, drivers' attention levels were maintained, and their feeling of safety was improved when both HMIs were used in combination.

Convolutional Neural Network-Based Lane-Change Strategy via Motion Image Representation for Automated and Connected Vehicles.

The lane-change decision-making module of automated and connected vehicles (ACVs) is one of the most crucial and challenging issues to be addressed. Motivated by human beings' underlying driving paradigm and the convolutional neural network's (CNN) dramatic capability of extracting features and learning strategies, this article proposes a CNN-based lane-change decision-making method via the dynamic motion image representation. Human drivers take proper driving maneuvers after they subconsciously construct the dynamic traffic scene representation in their brains, so this study first proposes the dynamic motion image representation method to reveal informative traffic situations in the motion-sensitive area (MSA), which provides a full view of surrounding cars. Then, this article develops a CNN model to extract the underlying features and learn driving policies from labeled datasets of MSA motion images. Besides, a safety-constrained layer is added to avoid vehicle collisions. We build a simulation platform based on the simulation of urban mobility (SUMO) to collect traffic datasets and test our proposed method. In addition, real-world traffic datasets are also involved to further investigate the proposed method's performance. The rule-based strategy and reinforcement learning (RL)-based method are used to compare with our approach. All results demonstrate that the proposed method performs lane-change decision-making much better than prevailing methods, which suggests our scheme has huge potential to accelerate the deployment of ACVs and is worth further study.

Impact of duration of monitoring before takeover request on takeover time with insights into eye tracking data.

Safety has become the primary concern of automated driving system (ADS) in recent years. Compared with highly automated driving (L4 and above), conditionally automated driving (L3/L3+ ADS) seems to be a moderate choice, where drivers are required to respond to the takeover request (TOR) whenever necessary. It is the system's responsibility to make sure that the takeovers would be safe at the time of issuing the TOR. To realize that, a lot of factors need to be taken into consideration. As it has been found that drivers' eyes-on-road gazes increase slowly in the first few seconds while transferring to manual driving from automated driving and drivers' gaze behaviors are related with situation awareness, the main aim of this study is to investigate the impact of duration of monitoring before the TOR on takeover time and whether there is a positive or negative relationship between the two. To verify these, we designed a driving simulator study where the TOR was issued 0 s, 5 s, 10 s and ≥ 15 s after the non-driving-related task has ended. Twelve scenarios were designed, and the results from 36 participants showed that there was indeed a statistically significant difference, however, the relationship was neither positive nor negative, which was close to a parabola. Analyzing results of eye movements and gaze behavior further supported this conclusion. It is therefore concluded the duration of monitoring before the TOR should neither be too short nor too long, and 5-7 s would be appropriate choices. This is desirable not only for improving takeover performance of drivers but also for improving the prediction model for predicting takeover performance of drivers that has yet to be studied, so as to improve safety, reliability and acceptance of the ADS.

A Predictive Model of a Driver's Target Trajectory Based on Estimated Driving Behaviors.

With the development of automated driving, inferring a driver's behavior can be a key element for designing an Advanced Driver Assistance System (ADAS). Current research is focused on describing and predicting a driver's behaviors as labels, e.g., lane shifting, lane keeping, etc., during driving. In our work, we consider that predicting a driver's behavior can be described as predicting a trajectory the driver may follow in the near future. The target trajectory can be calculated through certain polynomial functions. Via the data set collected by a Driving Simulator experiment covering nine volunteers, we proposed a model based on a deep learning network which is capable of predicting the corresponding coefficients of polynomial functions and then generating the trajectories in the next few seconds. The results also discussed and analyzed some possible factors affecting the prediction error. In conclusion, the model proved to be effective in predicting the target trajectory of a driver.

Quantitative Evaluation Methodology for Chassis-Domain Dynamics Performance of Automated Vehicles.

Thorough performance evaluation of automated vehicles (AVs) is an essential prerequisite for AVs' release and deployment. The challenges posed by dynamics performance appraisal of AVs are centered around the complexity of chassis dynamics, performance diversity, and lack of unified quantitative metrics. Therefore, this article proposes a novel quantitative evaluation metric for AVs' chassis-domain performance. We reveal mathematically explicit chassis steady boundaries of various vehicle maneuvers based on the modeling of chassis-domain dynamics and vehicle spatiotemporal signal analysis for safety-critical AVs. By defining and analyzing the multiperformance appraisal problem, this article gives mathematically prerequisites for evaluation metrics. Then, a rigorous metric is developed to quantify AVs' safety and comfort performance comprehensively. Wherein, the steady boundaries are leveraged to the metric normalization. We demonstrate the effectiveness of the proposed quantitative evaluation methodology in various scenarios. Test results illustrate that the proposed method provides a quantitative way to test AVs' integrated dynamics performance.

Effects of penetration rates on the application of in-vehicle traffic lights at unsignalized intersections.

OBJECTIVE: In-vehicle traffic lights have been demonstrated as an effective method to assist drivers in crossing unsignalized intersections, by displaying virtual traffic lights inside vehicles based on vehicle-to-vehicle communications. However, previous studies assumed that all the vehicles were equipped with vehicular communications, of which the deployment might last for decades. Therefore, it is necessary to consider the application of the system in partial deployment scenarios. This study aimed to analyze the influences of in-vehicle traffic light system on driving safety at unsignalized intersections within different penetration rates. METHODS: Driving simulator experiments involving 18 participants were performed. Evaluation indexes including post-encroachment time, maximum acceleration stroke of ego vehicle, and times of near miss incidents were investigated for different penetration rates of vehicular communications. RESULTS: Significant differences were observed between the conditions when no in-vehicle traffic light was used and when the penetration rate was 100% for the post-encroachment time (p = 0.02), and the maximum acceleration stroke of ego vehicle (p = 0.05). Meanwhile, it was found that the post-encroachment time slightly decreased, and the times of near miss incidents slightly increased when the penetration rate increased from 0% to 50%. However, the post-encroachment time was still longer for different penetration rates, compared to the condition when no in-vehicle traffic light was applied. CONCLUSIONS: The results indicated that the in-vehicle traffic light system could significantly improve driving safety at unsignalized intersections when the penetration rate was 100%. For partial deployment conditions, it is essential to speed up the deployment of vehicular communication-based driver assistance systems within early deployment stage, especially when the penetration rate was within 50%, to ensure driving safety.

Effect of Fixed and sEMG-Based Adaptive Shared Steering Control on Distracted Driver Behavior.

Driver distraction is a well-known cause for traffic collisions worldwide. Studies have indicated that shared steering control, which actively provides haptic guidance torque on the steering wheel, effectively improves the performance of distracted drivers. Recently, adaptive shared steering control based on the forearm muscle activity of the driver has been developed, although its effect on distracted driver behavior remains unclear. To this end, a high-fidelity driving simulator experiment was conducted involving 18 participants performing double lane change tasks. The experimental conditions comprised two driver states: attentive and distracted. Under each condition, evaluations were performed on three types of haptic guidance: none (manual), fixed authority, and adaptive authority based on feedback from the forearm surface electromyography of the driver. Evaluation results indicated that, for both attentive and distracted drivers, haptic guidance with adaptive authority yielded lower driver workload and reduced lane departure risk than manual driving and fixed authority. Moreover, there was a tendency for distracted drivers to reduce grip strength on the steering wheel to follow the haptic guidance with fixed authority, resulting in a relatively shorter double lane change duration.

Surface Electromyography-Controlled Automobile Steering Assistance.

Disabilities of the upper limb, such as hemiplegia or upper limb amputation, can limit automobile drivers to steering with one healthy arm. For the benefit of these drivers, recent studies have developed prototype interfaces that realized surface electromyography (sEMG)-controlled steering assistance with path-following accuracy that has been validated with driving simulations. In contrast, the current study expands the application of sEMG-controlled steering assistance by validating the Myo armband, a mass-produced sEMG-based interface, with respect to the path-following accuracy of a commercially available automobile. It was hypothesized that one-handed remote steering with the Myo armband would be comparable or superior to the conventional operation of the automobile steering wheel. Although results of low-speed field testing indicate that the Myo armband had lower path-following accuracy than the steering wheel during a 90° turn and wide U-turn at twice the minimum turning radius, the Myo armband had superior path-following accuracy for a narrow U-turn at the minimum turning radius and a 45° turn. Given its overall comparability to the steering wheel, the Myo armband could be feasibly applied in future automobile studies.

Radiation modes and acoustic field confined near acoustic sources.

A method to confine the acoustic pressure distribution near a loudspeaker array is investigated for potential application to personal audio devices. The surface velocity distribution of the loudspeaker array is determined so that more reactive than active acoustic power can be generated. The design is based on the theory of radiation modes, i.e., surface velocity distributions that independently contribute to the complex acoustic power. A computer simulation is conducted for a rectangular array of pistons in an infinite baffle, and it is shown that a locally confined acoustic field can be achieved as expected.

Design Procedure and Experimental Verification of a Broadband Quad-Stable 2-DOF Vibration Energy Harvester.

Vibration-based energy harvesters brought the idea of self-powered sensors to reality in the past few years. Many strategies to improve the performance of linear vibration energy harvesters that collect energy over a limited bandwidth have been proposed. In this paper, a bi-stable two degrees of freedom (2-DOF) cut-out vibration energy harvester employing a pair of permanent magnets is designed through a proposed design methodology. Based on this methodology, the nonlinear harvesters can be optimally designed such that the bandwidth can be widened for a targeted output voltage. The proper selection of the harvester parameters as well as the gap distances between the tip and the fixed magnets are the bases of this methodology. The mathematical modeling of the proposed harvester and the formula for the potential energy between the tip and the fixed magnets are presented. Additionally, to enhance the performance of the bi-stable energy harvester (BEH), a quad-stable energy harvester (QEH) was configured by adding more fixed magnets. Experiments were performed to validate the numerical simulations and the results showed that, the simulation and experimental results are consistent. The results indicate that, the QEH covers a wider bandwidth than the BEH and based on a figure of merit the QEH shows the best performance among many harvesters presented in the literature.

Design and Evaluation of a Surface Electromyography-Controlled Steering Assistance Interface.

Millions of drivers could experience shoulder muscle overload when rapidly rotating steering wheels and reduced steering ability at increased steering wheel angles. In order to address these issues for drivers with disability, surface electromyography (sEMG) sensors measuring biceps brachii muscle activity were incorporated into a steering assistance system for remote steering wheel rotation. The path-following accuracy of the sEMG interface with respect to a game steering wheel was evaluated through driving simulator trials. Human participants executed U-turns with differing radii of curvature. For a radius of curvature equal to the minimum vehicle turning radius of 3.6 m, the sEMG interface had significantly greater accuracy than the game steering wheel, with intertrial median lateral errors of 0.5 m and 1.2 m, respectively. For a U-turn with a radius of 7.2 m, the sEMG interface and game steering wheel were comparable in accuracy, with respective intertrial median lateral errors of 1.6 m and 1.4 m. The findings of this study could be utilized to realize accurate sEMG-controlled automobile steering for persons with disability.

Effectiveness Testing of a Piezoelectric Energy Harvester for an Automobile Wheel Using Stochastic Resonance.

The collection of clean power from ambient vibrations is considered a promising method for energy harvesting. For the case of wheel rotation, the present study investigates the effectiveness of a piezoelectric energy harvester, with the application of stochastic resonance to optimize the efficiency of energy harvesting. It is hypothesized that when the wheel rotates at variable speeds, the energy harvester is subjected to on-road noise as ambient excitations and a tangentially acting gravity force as a periodic modulation force, which can stimulate stochastic resonance. The energy harvester was miniaturized with a bistable cantilever structure, and the on-road noise was measured for the implementation of a vibrator in an experimental setting. A validation experiment revealed that the harvesting system was optimized to capture power that was approximately 12 times that captured under only on-road noise excitation and 50 times that captured under only the periodic gravity force. Moreover, the investigation of up-sweep excitations with increasing rotational frequency confirmed that stochastic resonance is effective in optimizing the performance of the energy harvester, with a certain bandwidth of vehicle speeds. An actual-vehicle experiment validates that the prototype harvester using stochastic resonance is capable of improving power generation performance for practical tire application.

PARAFAC Decomposition for Ultrasonic Wave Sensing of Fiber Bragg Grating Sensors: Procedure and Evaluation.

Ultrasonic wave-sensing technology has been applied for the health monitoring of composite structures, using normal fiber Bragg grating (FBG) sensors with a high-speed wavelength interrogation system of arrayed waveguide grating (AWG) filters; however, researchers are required to average thousands of repeated measurements to distinguish significant signals. To resolve this bottleneck problem, this study established a signal-processing strategy that improves the signal-to-noise ratio for the one-time measured signal of ultrasonic waves, by application of parallel factor analysis (PARAFAC) technology that produces unique multiway decomposition without additional orthogonal or independent constraints. Through bandpass processing of the AWG filter and complex wavelet transforms, ultrasonic wave signals are preprocessed as time, phase, and frequency profiles, and then decomposed into a series of conceptual three-way atoms by PARAFAC. While an ultrasonic wave results in a Bragg wavelength shift, antiphase fluctuations can be observed at two adjacent AWG ports. Thereby, concentrating on antiphase features among the three-way atoms, a fitting atom can be chosen and then restored to three-way profiles as a final result. An experimental study has revealed that the final result is consistent with the conventional 1024-data averaging signal, and relative error evaluation has indicated that the signal-to-noise ratio of ultrasonic waves can be significantly improved.

Greater Activity in the Frontal Cortex on Left Curves: A Vector-Based fNIRS Study of Left and Right Curve Driving.

OBJECTIVES: In the brain, the mechanisms of attention to the left and the right are known to be different. It is possible that brain activity when driving also differs with different horizontal road alignments (left or right curves), but little is known about this. We found driver brain activity to be different when driving on left and right curves, in an experiment using a large-scale driving simulator and functional near-infrared spectroscopy (fNIRS). RESEARCH DESIGN AND METHODS: The participants were fifteen healthy adults. We created a course simulating an expressway, comprising straight line driving and gentle left and right curves, and monitored the participants under driving conditions, in which they drove at a constant speed of 100 km/h, and under non-driving conditions, in which they simply watched the screen (visual task). Changes in hemoglobin concentrations were monitored at 48 channels including the prefrontal cortex, the premotor cortex, the primary motor cortex and the parietal cortex. From orthogonal vectors of changes in deoxyhemoglobin and changes in oxyhemoglobin, we calculated changes in cerebral oxygen exchange, reflecting neural activity, and statistically compared the resulting values from the right and left curve sections. RESULTS: Under driving conditions, there were no sites where cerebral oxygen exchange increased significantly more during right curves than during left curves (p > 0.05), but cerebral oxygen exchange increased significantly more during left curves (p < 0.05) in the right premotor cortex, the right frontal eye field and the bilateral prefrontal cortex. Under non-driving conditions, increases were significantly greater during left curves (p < 0.05) only in the right frontal eye field. CONCLUSIONS: Left curve driving was thus found to require more brain activity at multiple sites, suggesting that left curve driving may require more visual attention than right curve driving. The right frontal eye field was activated under both driving and non-driving conditions.

Biosignal analysis to assess mental stress in automatic driving of trucks: palmar perspiration and masseter electromyography.

Nowadays insight into human-machine interaction is a critical topic with the large-scale development of intelligent vehicles. Biosignal analysis can provide a deeper understanding of driver behaviors that may indicate rationally practical use of the automatic technology. Therefore, this study concentrates on biosignal analysis to quantitatively evaluate mental stress of drivers during automatic driving of trucks, with vehicles set at a closed gap distance apart to reduce air resistance to save energy consumption. By application of two wearable sensor systems, a continuous measurement was realized for palmar perspiration and masseter electromyography, and a biosignal processing method was proposed to assess mental stress levels. In a driving simulator experiment, ten participants completed automatic driving with 4, 8, and 12 m gap distances from the preceding vehicle, and manual driving with about 25 m gap distance as a reference. It was found that mental stress significantly increased when the gap distances decreased, and an abrupt increase in mental stress of drivers was also observed accompanying a sudden change of the gap distance during automatic driving, which corresponded to significantly higher ride discomfort according to subjective reports.

Leukoaraiosis significantly worsens driving performance of ordinary older drivers.

BACKGROUND: Leukoaraiosis is defined as extracellular space caused mainly by atherosclerotic or demyelinated changes in the brain tissue and is commonly found in the brains of healthy older people. A significant association between leukoaraiosis and traffic crashes was reported in our previous study; however, the reason for this is still unclear. METHOD: This paper presents a comprehensive evaluation of driving performance in ordinary older drivers with leukoaraiosis. First, the degree of leukoaraiosis was examined in 33 participants, who underwent an actual-vehicle driving examination on a standard driving course, and a driver skill rating was also collected while the driver carried out a paced auditory serial addition test, which is a calculating task given verbally. At the same time, a steering entropy method was used to estimate steering operation performance. RESULTS: The experimental results indicated that a normal older driver with leukoaraiosis was readily affected by external disturbances and made more operation errors and steered less smoothly than one without leukoaraiosis during driving; at the same time, their steering skill significantly deteriorated. CONCLUSIONS: Leukoaraiosis worsens the driving performance of older drivers because of their increased vulnerability to distraction.

Effective suppression of hippocampal seizures in rats by direct hippocampal cooling with a Peltier chip.

OBJECT: The use of focal brain cooling to eliminate epileptic discharges (EDs) has attracted increasing attention in the scientific community. In this study, the inhibitory effect of selective hippocampal cooling on experimental hippocampal seizures was investigated using a newly devised cooling system with a thermoelectric (Peltier) chip. METHODS: A copper needle coated with silicone and attached to the Peltier chip was used for the cooling device. The experiments were performed first in a phantom model with thermography and second in adult male Sprague-Dawley rats in a state of halothane anesthesia. The cooling needle, a thermocouple, and a needle electrode for electroencephalography recording were inserted into the right hippocampus. Kainic acid (KA) was injected into the right hippocampus to provoke the EDs. The animals were divided into hippocampal cooling (10 rats) and noncooling (control, 10 rats) groups. RESULTS: In the phantom study, the cooling effects (9 degrees C) occurred in the spherical areas around the needle tip. In the rats the temperature of the cooled hippocampus decreased below 20 degrees C within a 1.6-mm radius and below 25 degrees C within a 2.4-mm radius from the cooling center. The temperature at the needle tip decreased below 20 degrees C within 1 minute and was maintained at the same level until the end of the cooling process. The amplitude of the EDs was suppressed to 68.1 +/- 4.8% of the precooling value and remained low thereafter. No histological damage due to cooling was observed in the rat hippocampus. CONCLUSIONS: Selective hippocampal cooling effectively suppresses the KA-induced hippocampal EDs. Direct hippocampal cooling with a permanently implantable system is potentially useful as a minimally invasive therapy for temporal lobe epilepsy and therefore could be an alternative to the temporal lobectomy.

Use of a Peltier chip with a newly devised local brain-cooling system for neocortical seizures in the rat. Technical note.

Local cortical cooling for termination of epileptic discharges (EDs) has recently become a focus of research. The authors report on a newly devised cooling system that uses a thermoelectric (Peltier) chip and examine the system's performance in experimental neocortical seizures. Experiments were performed in adult male Sprague-Dawley rats after induction of halothane anesthesia. The Peltier chip was attached to a heat sink with a water channel. Two silicon tubes were connected to the heat sink, and water at 37 degrees C was circulated in the channel. The newly designed device was placed on the surface of the cortex. Kainic acid (KA) was injected into the cortex to provoke EDs. In the nonepileptic cortex, the temperature of the cortical surface decreased to 14.8 +/- 1.5 degrees C and that 2 mm below the surface to 27.1 +/- 3.1 degrees C within 30 seconds after the start of cooling. The temperature of the heated side of the chip was maintained at approximately 36.9 degrees C. Without water circulation, the temperature of the cortical surface decreased to 20 degrees C but soon began to increase, peaking at 30 degrees C. The temperature of the heated side of the chip rose to more than 60 degrees C. The EDs, which appeared within 20 minutes after KA injection, began to decrease in amplitude immediately after cooling began and continued to decrease as the temperature of the cortex was lowered. Sufficient miniaturization and good performance of the cooling device was demonstrated. Further efforts to develop implantable cooling systems and improve existing ones should be continued.