Driver Response Research Articles

DROID: Driver-Centric Risk Object IDentification.

Identification of high-risk driving situations is generally approached through collision risk estimation or accident pattern recognition. In this work, we approach the problem from the perspective of subjective risk. We operationalize subjective risk assessment by predicting driver behavior changes and identifying the cause of changes. To this end, we introduce a new task called driver-centric risk object identification (DROID), which uses egocentric video to identify object(s) influencing a driver's behavior, given only the driver's response as the supervision signal. We formulate the task as a cause-effect problem and present a novel two-stage DROID framework, taking inspiration from models of situation awareness and causal inference. A subset of data constructed from the Honda Research Institute Driving Dataset (HDD) is used to evaluate DROID. We demonstrate state-of-the-art DROID performance, even compared with strong baseline models using this dataset. Additionally, we conduct extensive ablative studies to justify our design choices. Moreover, we demonstrate the applicability of DROID for risk assessment.

Bringing a Vehicle to a Controlled Stop: Effectiveness of a Dual-Control Scheme for Identifying Driver Drowsiness and Executing Safety Control under Hands-off Partial Driving Automation

This study proposes a strategy to bring the vehicle to a controlled stop if the driver fails to supervise the partial driving automation. In scenarios where the driving context changes, the system provides information on the object/event detection and response, and it observes the driver's response (i.e., pressing a button). If the driver fails to respond to the given information within 15 seconds, the system implements a first-stage control that reduces the speed to 50 km/h. If the driver does not perform the accelerator action within 30 seconds after first-stage control, the system determines that “the driver failed to supervise the partial driving automation system”. To demonstrate the effectiveness of the proposed system for identifying driver drowsiness and implementing deceleration control, a total of 26 drivers participated in an experiment. In 12 among 23 instances in which first-stage control was implemented, the system could successfully determine that “the driver failed to supervise the partial driving automation.” In 8 of the 12 instances, the vehicle was brought to a complete stop. We conclude that the system could be used effectively for safety control and for identifying the driver state.

Application of simulation methods on vibration analysis of a bus driver seat using the quarter car 3DOF model subjected under harmonic excitation

Vibration of bus driver's seat subjected under harmonic excitation, 3DOF (3 degrees of freedom) dynamic model is employed to carry out the calculation by 3 simulation methods. Mathematical model - numerical using Matlab programming language (Numerical model), Multi Body Dynamics (MBD) using Matlab Simmechanics tool (MBD model) and finite element model using Ansys APDL (FEM model) are employed. The strengths and limitations of each method are analyzed through the process of building models and retrieving the resulting data. The evaluation indexes include the response of driver's seat acceleration, response of the seat suspension's relative displacement and the seat suspension's vibration isolation ability according to two investigated cases of natural frequency and damping ratio. The obtained results from all 3 methods have no difference and the evaluation indexes are suitable according to the studies on vehicle vibration response. The Numerical model method requires the user to perform complex mathematical modeling. The MBD model method using Matlab Simmechanics is simpler, using built-in function blocks, requiring declarations to establish the exact initial equilibrium position of the mass elements. The FEM model method using Ansys APDL with keypoints and lines describing the model elements requires precise setting of the displacement of each keypoint. However, both the MBD and FEM model methods are only effective when investigating the response in the time domain for a particular instance of the excitation frequency. When analyzing the response in the frequency domain, which requires accessing and synthesizing data of many cases in the investigated frequency range, these two methods have many limitations in the calculation process, specifically, in performing complex computations with many loops and variables. Solving problems also takes longer than numerical method using mathematical model. The obtained results are analyzed in the time domain, frequency domain, and the value domain of the driver's seat suspension parameters. This result serves as a basis for developing the design of the driver's seat suspension system with nonlinear spring and shock absorber, and the selection of a calculation method suitable for each problem in the field of vehicle vibration.

Evaluating Driver Response to an Advanced Speed Display near Uncontrolled Median Openings

Uncontrolled median openings contribute to more road crashes because drivers do not obey traffic laws such as driving between lanes and following speed limits. This study examined the effectiveness of the different types of signs near an uncontrolled median opening in Mahmoud Abad, Iran. There were Signs 1 and 2 with a single message, while Signs 3 and 4 contained multi-messages. An Internet of Things (IoT) test system was developed to collect and record vehicle speed data before and after each sign installation. Results showed that speeding behavior decreased almost immediately after the road signs were installed and remained almost constant until the beginning of uncontrolled median openings. Moreover, multi-message traffic signs reduce vehicle speeds twice as much as single-message signs. Additionally, only multi-message signs are effective at reducing speed limit violations.

A Computational Cognitive Model of Driver Response Time for Scheduled Freeway Exiting Takeovers in Conditionally Automated Vehicles.

This study develops a computational model to predict drivers' response time and understand the underlying cognitive mechanism for freeway exiting takeovers in conditionally automated vehicles (AVs). Previous research has modeled drivers' takeover response time in emergency scenarios that demand a quick response. However, existing models may not be applicable for scheduled, non-time-critical takeovers as drivers take longer to resume control when there is no time pressure. A model of driver response time in non-time-critical takeovers is lacking. A computational cognitive model of driver takeover response time is developed based on Queuing Network-Model Human Processor (QN-MHP) architecture. The model quantifies gaze redirection in response to takeover request (ToR), task prioritization, driver situation awareness, and driver trust to address the complexities of drivers' takeover strategies when sufficient time budget exists. Experimental data of a preliminary driving simulator study were used to validate the model. The model accounted for 97% of the experimental takeover response time for freeway exiting. The current model can successfully predict drivers' response time for scheduled, non-time-critical freeway exiting takeovers in conditionally AVs. This model can be applied to the human-machine interface design with respect to ToR lead time for enhancing safe freeway exiting takeovers in conditionally AVs. It also provides a foundation for future modeling work towards an integrated driver model of freeway exiting takeover performance.

A simple metric for predicting the timing of river phytoplankton blooms

AbstractIn rivers, phytoplankton populations are continuously exported by unidirectional, advective flow. Both transport and growth conditions determine periods of excess phytoplankton growth, or blooms, in a given reach. Phytoplankton abundance, however, has mainly been compared to the state of either growth or transport conditions alone rather than in tandem. Previous studies have not yielded consistent driver–response relationships, limiting our ability to predict the timing of riverine phytoplankton blooms based on environmental factors. Here, we derive a simple joint metric that combines the state of growth and transport conditions, specifically the ratio of temperature and discharge (). We then compare the metric to biomass abundance data (daily, sensor‐based chlorophyll a [chl a] data) from a mid‐sized Great Plains river (the Kansas). was an excellent predictor of low to high biomass, outperforming either variable alone. However, it could not differentiate between very high biomass values, values well above the biomass threshold designating bloom conditions. Our findings of reduced performance at the highest values of T/Q indicated that could predict the occurrence (timing) but not magnitude of phytoplankton blooms, and we used T/Q to correctly predict 71% of days when bloom conditions occurred. Analyzing chl a abundance with also revealed likely switching from transport and temperature to nutrient control. offers a simple tool for (1) predicting the timing of river phytoplankton blooms, (2) forecasting how river ecosystems will respond to surrounding environmental changes, and (3) determining which environmental factors shape phytoplankton blooms at specific locations along a river.

Responses to Take-Over Request in Autonomous Vehicles: Effects of Environmental Conditions and Cues

Conditional autonomous vehicles have attracted significant attention, and their wide-spread use is expected to further increase in the near future. However, such vehicles can send take-over requests (TORs) to a driver who may struggle to accommodate the request while immersed in non-driving-related tasks. Previous studies have focused on TOR times and cues; however, the effects of environmental conditions have not been examined rigorously. This study, with the aim of addressing the aforementioned issue, is divided into two parts: in Study 1, we examine driver responses to TORs under different environmental conditions (i.e., sunny, rainy, snowy, foggy, and night-time), and in Study 2, we examine the effects of the proposed TOR cues (augmented reality + smartphone alert) under different environmental conditions. For this investigation, a driving simulator was used for the participants’ safety. Each study involves 33 participants. The results of Study 1 indicate significant differences in the take-over time, lane-change time, time-to-collision, maximum acceleration, and subjective mental workload corresponding to different environmental conditions. Furthermore, the results of Study 2 suggest that the proposed TOR cues significantly reduce the effects of environmental conditions on various take-over performances. We discuss the implication of these results in terms of the improvements in responses to TORs and investigation of the effects of environmental conditions on the responses to TORs.

Traffic signal control in mixed traffic environment based on advance decision and reinforcement learning

Abstract Reinforcement learning-based traffic signal control systems (RLTSC) can enhance dynamic adaptability, save vehicle travelling time and promote intersection capacity. However, the existing RLTSC methods do not consider the driver's response time requirement, so the systems often face efficiency limitations and implementation difficulties. We propose the advance decision-making reinforcement learning traffic signal control (AD-RLTSC) algorithm to improve traffic efficiency while ensuring safety in mixed traffic environment. First, the relationship between the intersection perception range and the signal control period is established and the trust region state (TRS) is proposed. Then, the scalable state matrix is dynamically adjusted to decide the future signal light status. The decision will be displayed to the human-driven vehicles (HDVs) through the bi-countdown timer mechanism and sent to the nearby connected automated vehicles (CAVs) using the wireless network rather than be executed immediately. HDVs and CAVs optimize the driving speed based on the remaining green (or red) time. Besides, the Double Dueling Deep Q-learning Network algorithm is used for reinforcement learning training; a standardized reward is proposed to enhance the performance of intersection control and prioritized experience replay is adopted to improve sample utilization. The experimental results on vehicle micro-behaviour and traffic macro-efficiency showed that the proposed AD-RLTSC algorithm can simultaneously improve both traffic efficiency and traffic flow stability.

Strategic collaboration between land owners and charging station operators: Lease or outsource?

Many regions of the world aim to phase out conventional private gasoline cars within ten years, requiring imminent expansion of the electric vehicle (EV) charging infrastructure. However, because of high installation and maintenance costs, many landowners (LOs), such as shopping malls and commercial building owners, hesitate to install and operate large-scale EV charging facilities of their own volition. In light of this reason, government-led investment is widely provided in many cities, in which local governments provide subsidies to or directly invest in the installation of EV charging facilities. However, this mode cannot be sustained in the long run. As the charging infrastructure market grows, many charging station operators (CSOs), who specialize in EV charging facility installation and operation, have emerged in recent years. These CSOs rent parking spaces from LOs to operate their charging business, which can potentially benefit LOs by attracting more EV drivers to patronize LOs. This parking-charging integration scheme (P&C scheme) builds a symbiotic relationship between LOs and CSOs. However, such schemes may have negative impacts on LOs. Allocating parking spaces to CSOs will increase the time general consumers without charging needs spend searching for parking, which will reduce the number of general consumers. If the newly attracted EV drivers only charge but do not consume in the LO, the LO might not be better off by participating in this scheme. To analyze the economic viability of the P&C scheme and the resulting impact on consumers, we develop a sequential-move game model, taking into account the responses of general consumers and EV drivers. We analytically prove the existence and uniqueness of the market equilibrium and derive the feasibility conditions for this scheme. Interestingly, we find that when the proportion of charging drivers who choose to patronize the LO is high, the LO is willing to reward the CSO for operating the charging service. A realistic P&C example is also presented to demonstrate our analytical insights.

Fault-tolerant human-machine shared scheme for collision avoidance of intelligent vehicles considering driver error and actuator fault

This paper presents a fault-tolerant human-machine shared scheme for collision avoidance considering driver error and actuator fault. A fault-tolerant method of trajectory planning and decision-making is developed for dealing with the actuator fault. We calculate the remainder trajectory tracking capability of the faulted actuator and utilise the motion redundancy in the system to generate feasible fault-tolerant trajectories for collision avoidance. In addition, an assessment criterion of driver error is conducted to supervise the driver's steering response to the potential collision and actuator fault. The shared system dynamically adjusts the driving authority based on the driver error. A model predictive control (MPC) algorithm is employed for tracking the fault-tolerant trajectory. Simulation and hardware-in-the-loop (HIL) experiment results demonstrate the effectiveness of the presented approach, reducing traffic accidents due to driver error and actuator fault.

Predictive Models of Driver Deceleration and Acceleration Responses to Lead Vehicle Cutting In and Out

A common maneuver drivers perform and experience on the road is changing lanes. Autonomous vehicles are required to engage a lane change safely and to react to the other road users’ lane changes. To develop autonomous vehicles that change lanes or respond to the lead vehicle’s lane changes in a safe and human-like way, one should investigate the factors that affect human driver responses. By reviewing the literature to identify potential factors, this study extracted these factors from a naturalistic driving data set and associated them with driver deceleration and acceleration responses to the lead vehicle’s cut-in and cut-out to develop predictive models for the impact of the events on traffic flow. After the events were verified as accurate, the variables associated with the events, including range, range rate, speed, lateral position in the lane, and average acceleration were analyzed using logistic regression, support vector machines (SVM), and two forms of decision trees. In total, 799 cut-in events and 684 cut-out events with the necessary variables were applied for analysis. The significant variables influencing driver behavior were found, and using these variables, the predictive models achieved around 80% accuracy for cut-ins, and 73% accuracy for cut-outs on test data. These results will assist in the future design of autonomous vehicle control to minimize detrimental effects on traffic when changing lanes and safe longitudinal control when responding to a lead vehicle’s lane changes, allowing for safe integration with human drivers, and better design of driver assistance systems.

: On growth patterns and mechanisms in arctic-alpine shrubs

Arctic-alpine ecosystems are considered hot-spots of environmental change, with rapidly warming conditions causing massive alterations in vegetational structure. These changes and their environmental controls are highly complex and variable across spatial and temporal scales. Yet, despite their numerous implications for the global climate system, the underlying physiological processes and mechanisms at the individual plant scale are still little explored. Using hourly recordings of shrub stem diameter change provided by dendrometers, paired with on-site environmental conditions, enabled us to shed light on these processes. In this way, growth patterns in three widely distributed shrub species were assessed and linked to thermal and hygric conditions. We started our analysis with a close examination of one evergreen species under extreme environmental conditions, followed by a comparison of evergreen and deciduous species, and, finally, a comparative look at growth patterns across local micro-habitats. The results revealed distinct growth strategies, closely linked to species-specific water-use dynamics and cambial rhythms. Within the heterogenous alpine landscape these conditions were mainly attributed to the variation in local micro-habitats, defined by fine-scale topography and consequent variation in snow conditions and exposure. Thus, the overall growth success was mainly controlled by complex seasonal dynamics of soil moisture availability, snow conditions, and associated freeze–thaw cycles. It was therefore in many cases decoupled from governing regional climate signals. At the same time, exceedingly high summer temperatures were limiting shrub growth during the main growing season, resulting in more or less pronounced bimodal growth patterns, indicating potential growth limitation with on-going summer warming. While shrubs are currently able to maximize their growth success through a high level of adaptation to local micro-site conditions, their continued growth under rapidly changing environmental conditions is uncertain. However, our results suggest a high level of heterogeneity across spatial and temporal scales. Thus, broad-scale vegetational shifts can not be explained by a singular driver or uniform response pattern. Instead, fine-scale physiological processes and on-site near-ground environmental conditions have to be incorporated into our understanding of these changes.

Modeling Driver Responses to Automation Failures With Active Inference

Automated vehicle (AV) technologies promise to improve traffic safety and reduce driver workload in the near future. However, most current implementations have limited capabilities and require transition of control between the vehicle and the human driver during automation failures. For this reason, models of driver behavior have been widely studied to assist the design and development of AV technologies. Recent works have shown that driver behavior models grounded in human cognitive information processing achieve better generalization than prior methods. In this work, we applied active inference, a framework of human perception, cognition, and behavior based on the predictive processing theory, to model driver emergency braking responses to automation failures. We estimated the model parameters from experimental data and examined the model parameters using a factor analysis. We verified the model’s braking response prediction capability in counterfactual scenarios. Our results show that the model effectively captured braking reaction times and provided insight on the correspondence between the variations in driver parameters and behavior.

Risk Assessment Methodologies for Autonomous Driving: A Survey

Autonomous driving systems (ADS) in recent years have been the subject of focus, evolving as one of the major mobility disruptors and being a potential candidate for deployment in urban cities due to urbanization. ADS is the system within the Autonomous Vehicle (AV) that enables automation. The different ADS technologies that enable autonomous vehicles have reached a certain maturity that no longer focus on technological deployment, but rather on the safe deployment on public roads. However, existing standards that validate functional safety and Risk Assessment (RA) may not be sufficient to tackle the increased complexity of ADS compared to traditional vehicles. This demand in ADS safety is exponentially increasing in tandem with the increase of AV automation levels. ADS are exposed to diverse environmental conditions and therefore subjected to operational risks while attempting to mimic the human driver responses. Moreover, the recent use of artificial intelligence and machine learning in the industry further shapes the way how ADS development will become in the future. This paper explains the importance of RA coverage for AV and provides a comparison and summary of existing RA methodologies. Thereafter, a recommendation of RAs for AV as potential solutions in meeting ISO 26262 and ISO/PAS 21448 standards.

Response of eutrophication and water quality drivers on greenhouse gas emissions in lakes of China: A critical analysis

AbstractLakes, especially shallow lakes, contribute disproportionately to greenhouse gas (GHG; particularly CO2 and CH4) emissions and have received global attention due to their high potential to contribute to global warming and future climate change. Recent studies have identified eutrophication as a critical factor in GHG emissions. However, the role of lake trophic state index (TSI) and the impact of important water quality parameters (WQP) such as pH, Chl‐a, total nitrogen, total phosphorus and organic carbon on GHG emissions are still a subject of debate and an area of intense research. To further understand the relationship between GHG and lake eutrophication, datasets (GHG and WQP) from the scientific literature have been compiled, and statistical analyses of these secondary data were performed to determine the influence of eutrophication on GHG emissions. In this review, GHG emissions from Chinese lakes are quantified, and the important factors affecting these emissions are analysed systematically. The statistical analysis reveals that chlorophyll a and carbon (as TOC) are the key factors of lake eutrophication and have a significant effect on the GHG potential (mainly CH4 fluxes). In addition, the proposed mitigation measures could serve as a guide for scientists and young researchers to reduce future climatic risks.

A Car-Following Model for Mixed Traffic Flows in Intelligent Connected Vehicle Environment Considering Driver Response Characteristics

Autonomous driving technology and vehicle-to-vehicle communication technology make the hybrid driving of connected and automated vehicles (CAVs) and regular vehicles (RVs) a long-existing phenomenon in the coming future. Among the existing studies, IDM models are mostly used to study the performance of homogeneous traffic flow. To explore the stability of mixed traffic flow, an extended intelligent driver model (IDM) based car-following model was proposed for mixed traffic flow (MTF) with both CAVs and RVs, considering the headway, the speed and acceleration of multiple front vehicles, as well as the response characteristics of RV drivers. Through the linear stability analysis, the criterion for the stability of MTFs was derived, and the relationship among the penetration rate of CAVs, equilibrium velocity and traffic stability in MTF are discussed. Based on the above theoretical model, a numerical simulation was conducted in two typical scenarios of starting and braking. The results showed that, at the microscopic scale, the vehicle in the Cooperative Adaptive Cruise Control (CACC) mode could significantly decelerate in response to the interference from other vehicles in the same traffic environment. At the macroscopic scale, as the penetration rate of CAVs increased, the overall acceleration fluctuation of the traffic flow decreased. At the same penetration rate of CAVs, the higher density of CAVs coincided with the higher stability of the MTF. When the penetration rate of CAVs was 50%, the degree of distribution had the greatest impact on the MTF. When the penetration rate of CAVs exceeded 70%, the degree of distribution had little impact on the MTF. This research can provide basic theoretical support for the management and control of MTF in the future.

How do drivers respond to vehicle cyberattacks? A driving simulator study

Modern vehicles are embedded with numerous electronic components, making them more advanced and automated, while also making them vulnerable to cyberattacks. This study investigated how drivers respond to unexpected, cyber-attack-induced situations through a driving simulator study. It also examined differences in driver responses if they were trained or received warning messages on how to mitigate the effect of a vehicle cyberattack. The findings suggest that drivers' responses to cyberattacks vary based on the severity of the event. Those who receive training are much more likely to drive cautiously when the vehicle behaves unexpectedly and those who receive warning messages are likely to view them, but not necessarily take action. These results have far reaching implications into the utility of training programs in improving driver behavior and leave future work in terms of optimizing warning message systems.

A Cognitive Computational Model of Driver Warning Response Performance in Connected Vehicle Systems

Most existing driver models focus on predicting driving performance in normal and near-collision situations without considering the impact of collision warning parameters on driver behavior. This study develops a cognitive computational driver model based on the Queueing Network-Model Human Processor (QN-MHP) to quantify the effects of key warning parameters (i.e., warning lead time, warning reliability, and speech warning style) on driver performance in warning responses, in connected vehicle systems (CVSs). The model was validated by comparing its predictions of driver response time, response type, and braking and steering performance with data from thirty-two drivers collected in an experimental study. Once the route choice mechanism had been implemented, the driver model was found to explain the cognitive mechanism underlying how drivers process warnings in CVSs. Indeed, the validation results showed that the model was able to capture major changes in patterns of the experimental data, with R-squared values of 0.88 for warning response time, 0.69 and 0.65 for decision making in response type for the initial trial and across trials, 0.85 for braking performance, and 0.83 for steering performance. The model can be applied to optimize the interface design of CVSs based on driver needs.

Driver Response to a Dynamic Speed Feedback Sign at a Freeway Exit Ramp Considering the Sign Design and Installation Characteristics

Research was conducted at a freeway exit ramp with significant horizontal curvature to evaluate the effectiveness of dynamic speed feedback signs (DSFSs) as a speed reduction countermeasure. Several aspects of the DSFSs were evaluated, including display size, border type, lateral installation position, and vehicle detection range. Three different full-matrix DSFSs were utilized, which included 15-in. display panel with yellow border, 18-in. display panel with yellow border, and 18-in. display panel with no border. Each sign was individually installed and tested at identical locations near the start of the exit ramp curve, in both the traditional right-side-mount and an alternative forward-mount within the exit gore area. Speed data and message activation location were collected for vehicles approaching and entering the curve across the various sign test conditions. Overall, the presence of a DSFS positioned near the start of the curve resulted in curve entry speeds that were, on average, 3.5 mph lower than without a DSFS present. The lowest curve entry speeds were observed for cases in which the message activated when vehicles were within 250 to 400 ft of the curve. Interestingly, earlier message activation did not contribute to further speed reductions, although later activation substantially diminished the speed reduction effects. With regard to DSFS lateral position, both the side-mounted and forward-mounted DSFS installations resulted in similar curve entry speeds. Furthermore, there were no discernable differences in curve entry speeds between the 15- and 18-in. display panels, although the inclusion of a yellow sign border had greater speed reduction effects.

Driver Response to a Dynamic Speed Feedback Sign at Speed Transition Zones Along High-Speed Rural Highways

Research was conducted to assess the effectiveness of a dynamic speed feedback sign (DSFS) as a speed reduction strategy at speed transition zones along five high-speed rural highways in northern Michigan. The DSFS was positioned on the shoulder in advance of the reduced speed limit at each community and was programmed to display speeds of approaching vehicles alternating with the upcoming reduced speed limit. Using handheld LIDAR guns, vehicle speeds were tracked through each speed transition area before and after installation of the DSFS. The DSFS was found to have a significant speed reduction effect throughout each of the five speed transition zones. The speed reductions generally began when the DSFS came into the motorists’ view, and the speed reduction effect increased as motorists approached DSFS. The greatest speed reduction effects were observed at the DSFS location itself, where speeds were 3.2 to 7.8 mph lower with the DSFS present, and these reductions were sustained on entry to the community. Similarly, drivers were 78.8 to 92.4% less likely to exceed the reduced speed limit with the DSFS present. Positioning the DSFS further upstream typically resulted in earlier speed reductions, although this effect became negligible once vehicles reached the reduced speed zone entering the community. Based on the study findings, continued use of DSFS for speed management at speed transition zones entering communities on high-speed rural roadways is recommended. When used in this context, the DSFS should be positioned 250 to 650 ft upstream of the reduced speed limit.