Shan Bao

Heavy-Truck Drivers’ Following Behavior With Intervention of an Integrated, In-Vehicle Crash Warning System: A Field Evaluation

Objective: This study is designed to evaluate heavy-truck drivers’ following behavior and how a crash warning system influences their headway maintenance. Background: Rear-end crashes are one of the major crash types involving heavy trucks and are more likely than other crash types to result in fatalities. Previous studies have observed positive effects of in-vehicle crash warning systems in passenger car drivers. Although heavy-truck drivers are generally more experienced, driver-related errors are still the leading factors contributing to heavy-truck-related rear-end crashes. Method: Data from a 10-month naturalistic driving study were used. Participants were 18 professional heavy-truck drivers who received warnings during the last 8 months of the study (treatment period) but not during the first 2 months (baseline period). Time headway and driver’s brake reaction time were extracted and compared with condition variables, including one between-subjects variable (driver shift) and five within-subjects variables (treatment condition, roadway types, traffic density, wiper state, and trailer configuration). Results: The presence of warnings resulted in a 0.28-s increase of mean time headway with dense on-road traffic and a 0.20-s increase with wipers on. Drivers also responded to the forward conflicts significantly faster (by 0.26 s, a 15% enhancement) in the treatment condition compared with responses in the baseline condition. Conclusion: Positive effects on heavy-truck drivers’ following performance were observed with the warning system. Application: The installation of such in-vehicle crash warning systems can help heavy-truck drivers keep longer headway distances in challenging situations and respond quicker to potential traffic conflicts, therefore possibly increasing heavy-truck longitudinal driving safety.

Using naturalistic driving data to examine drivers' seatbelt use behavior: Comparison between teens and adults.

PROBLEM Teens and young drivers are often reported as one driver group that has significantly lower seatbelt use rates than other age groups. OBJECTIVE This study was designed to address the questions of whether and how seatbelt-use behavior of novice teen drivers is different from young adult drivers and other adult drivers when driving on real roads. METHOD Driving data from 148 drivers who participated in two previous naturalistic driving studies were further analyzed. The combined dataset represents 313,500 miles, 37,695 valid trips, and about 9500 h of driving. Drivers did not wear their seatbelts at all during 1284 trips. Two dependent variables were calculated, whether and when drivers used seatbelts during a trip, and analyzed using logistic regression models. RESULTS Results of this study found significant differences in the likelihood of seatbelt use between novice teen drivers and each of the three adult groups. Novice teen drivers who recently received their drivers licenses were the most likely to use a seatbelt, followed by older drivers, middle-aged drivers, and young drivers. Young drivers were the least likely to use a seatbelt. Older drivers were also more likely to use seatbelts than the other two adult groups. The results also showed that novice teen drivers were more likely to fasten their seatbelts at the beginning of a trip when compared to the other three adult groups. SUMMARY Novice teen drivers who were still in the first year after obtaining their drivers license were the most conservative seatbelt users, when compared to adult drivers. PRACTICAL APPLICATION Findings from this study have practical application insights in both developing training programs for novice teen drivers and designing seatbelt reminder and interlock systems to promote seatbelt use in certain driver groups.

Longitudinal driving behavior with integrated crash-warning system

This study created the most extensive set of naturalistic data that has ever been gathered on the following behavior of drivers when interacting with a forward crash-warning system. For the purposes of this paper, data from the naturalistic driving study of the Integrated Vehicle-Based Safety System (IVBSS) program were used. IVBSS data collected from a total of 108 drivers, representing 81,163 steady state following events and 20,096 forward conflict events were extracted and compared. Drivers were from three age groups (younger, middle-aged, and older) and balanced between two gender groups. Three objective measures were used in this study: mean time headway, minimum time to collision, and proportion of time drivers spent in time headway of 1 s or less. Drivers used the research vehicles for 40 days, with the system not activated for the first 12 days and activated for the following 28 days. A linear mixed model was used for the data analysis. Results of this study show that drivers have a tendency to follow more closely when the warning system is activated. It is recommended that a visual display for feedback on real-time safe following distance may help drivers keep a safer distance. This study also observed age-related self-regulation behavior when other vehicles were being followed and showed that older drivers tended to follow farther away from the leading vehicle.

Gap Acceptance During Lane Changes by Large-Truck Drivers—An Image-Based Analysis

This paper presents an analysis of rearward gap acceptance characteristics of drivers of large trucks in highway lane change scenarios. The range between the vehicles was inferred from camera images using the estimated lane width obtained from the lane tracking camera as the reference. Six-hundred lane change events were acquired from a large-scale naturalistic driving data set. The kinematic variables from the image-based gap analysis were filtered by the weighted linear least squares in order to extrapolate them at the lane change time. In addition, the time-to-collision and required deceleration were computed, and potential safety threshold values are provided. The resulting range and range rate distributions showed directional discrepancies, i.e., in left lane changes, large trucks are often slower than other vehicles in the target lane, whereas they are usually faster in right lane changes. Video observations have confirmed that major motivations for changing lanes are different depending on the direction of move, i.e., moving to the left (faster) lane occurs due to a slower vehicle ahead or a merging vehicle on the right-hand side, whereas right lane changes are frequently made to return to the original lane after passing.

Accelerated Evaluation of Automated Vehicles in Lane Change Scenarios

It is important to rigorously and comprehensively evaluate the safety of Automated Vehicles (AVs) before their production and deployment. A popular AV evaluation approach is Naturalistic-Field Operational Test (N-FOT) which tests prototype vehicles directly on public roads. Due to the low exposure to safety-critical scenarios, N-FOTs is time-consuming and expensive to conduct. Computer simulations can be used as an alternative to N-FOTs, especially in terms of generating motions of the surrounding traffic. In this paper, we propose an accelerated evaluation approach for AVs. Human-controlled vehicles (HVs) were modeled as disturbance to AVs based on data extracted from the Safety Pilot Model Deployment Program. The cut-in scenarios are generated based on skewed statistics of collected human driver behavior, which amplifies riskier testing scenarios while reserves its statistical information so that the safety benefits of AV in non-accelerated cases can be accurately estimated. An AV model based on a production vehicle was tested. Results show that the proposed method can accelerate the evaluation process by at least 100 times.Copyright

Factors Affecting Drivers’ Cell Phone Use Behavior: Implications from a Naturalistic Study

The purpose of this study was to examine drivers’ cell phone use behavior as reflected in naturalistic driving data. Video data from 1 weeks worth of driving for 108 participants were visually scored for all instances of cell phone use, including conversation and visual or manual (VM) tasks. The frequency of cell phone use for each participant was used to classify drivers’ behavior. Three frequency groups (low, moderate, and high) were scored across all drivers for conversation and VM tasks separately. The regression tree method was used to classify drivers’ cell phone use behavior and identify associated factors. Drivers’ individual factors, including age, annual driving mileage, and education levels, as well as situational factors, including use duration, time of day, road type, lighting (day and night), traffic conditions, and speed when initiating cell phone use, impacted drivers’ cell phone use behavior. The impacts of these factors were different for cell phone conversation and VM tasks. Traffic conditions were identified as affecting drivers’ cell phone VM task use frequency but not cell phone conversation frequency. The study also looked at driver self-regulation behavior based on the frequency of cell phone use.

Reduction of Backing Crashes by Production Rear Vision Camera Systems

Today’s automotive Rear Vision Camera (RVC) systems display an image to the driver of an area behind the vehicle generated by a camera located in the rear of the vehicle. This paper examined if, and to what extent, these systems offered on a wide variety of production vehicles are addressing backing crashes (estimated to represent approximately 3%-4% of all annual police-reported crashes in the United States). Police-reported crashes from ten United States state crash databases were examined to determine the frequency of backing crashes and control (baseline) crashes. The logistic regression model developed suggests that production RVC systems examined may be reducing overall police-reported backing crashes by 52%. This is a particularly promising finding because these systems may also be helping to avoid additional backing crashes that have not been reported to the police. This research can be used to inform emerging crash avoidance system-related system consumer metrics (e.g., New Car Assessment Program (NCAP) programs), government regulations surrounding RVC systems, and system performance requirements associated with RVC consumer metrics and regulations.

Training and Education: Human Factors Considerations for Automated Driving Systems

Vehicles with partial automation, forerunners to those with higher levels of automation, are already being deployed by automakers. These current deployments, although incremental, have the potential to disrupt how people interact with vehicles. This chapter reports on a discussion of related issues that was held as part of the Human Factors Breakout session at the 2017 Automated Vehicle Symposium. The session, titled “Automated Vehicle Challenges: How can Human Factors Research Help Inform Designers, Road Users, and Policy Makers?”, included discussions between industry experts and human factors researchers and professionals on immediate human factors issues surrounding deployment of vehicles with Automated Driving Systems (ADS).

Spectral Power Analysis of Drivers’ Gas Pedal Control during Steady-state Car-following on Freeways:

This paper investigated the frequency characteristics of drivers’ gas pedal control in steady-state car-following on freeways by using vehicle sensor data from an existing naturalistic driving study. The main objectives were to examine the frequency range and distributions of a driver operating the gas pedal when following a lead vehicle, and whether the higher and lower frequency components of the gas pedal signal would vary when following a lead vehicle with varying distances. A total of 1,461 driving segments each with 90-seconds of steady-state freeway car-following were extracted from the naturalistic driving data. Fourier analysis was performed to convert the time series data of drivers’ gas pedal control to the frequency domain. The results show that during steady-state freeway car-following, the power of the gas pedal control peaks at around 0.033 Hz or 15 s per pedal movement (derived using the median of the peak frequency), and the upper limit of the frequency is around 0.94 Hz or 0.5 s per pedal movement (derived using the 95th percentile of the cutoff frequency). Further analysis showed that following a lead vehicle with smaller gap was associated with a larger proportion of the higher frequency component (p < .001), and following a lead vehicle with larger gap was associated with a larger proportion of the lower frequency component (p < .001). This suggests that the larger gap may allow the driver to relax control of the gas pedal with smoother operation. Potential applications of this paper include developing more realistic driver models that could be used in designing advanced driver assistance systems.

Automated Control and Brake Strategies for Future Crash Avoidance Systems - Potential Benefits

This paper explores the potential safety performance of “Future Generation” automated speed control crash avoidance systems for Commercial Vehicles. The technologies discussed in this paper include Adaptive Cruise Control (ACC), second and third generation Forward Collision Avoidance and Mitigation Systems (F-CAM) comprised of Forward Collision Warning (FCW) with Collision Mitigation Braking (CMB) technology as applied to heavy trucks, including single unit and tractor semitrailers. The research [1[ discussed in this paper is from a study conducted by UMTRI which estimated the safety benefits of current and future F-CAM systems and the comparative efficacy of adaptive cruise control. The future generation systems which are the focus of this paper were evaluated at two separate levels of product refinement, “second generation” and “third generation” systems. Second generation systems have the capability of reacting to fixed vehicles which were not moving prior to the engagement of the radar and include CMB nominal brake deceleration of 0.35g. Third generation systems to react to fixed vehicles as well but with a substantially more aggressive CMB brake deceleration capability of 0.6 g. The ACC system was evaluated with two levels of foundation brake performance, −0.25 g and −0.6 g The functional characteristics of a prototype future F-CAM system were evaluated and its performance generically modeled in the context of second generation and third generation attributes to estimate potential safety benefits. This was accomplished through the following steps: (1) first characterize the actual performance of the prototype future system in various pre-crash scenarios under controlled test track conditions, and then reverse engineering the algorithms that control warnings and automatic braking actions and adding second and third generation performance characteristics; (2) developing a comprehensive set of simulated crash events representative of actual truck striking rear-end crashes. This virtual “reference” crash database was developed by analyzing vehicle interactions (or conflicts) from naturalistic data to create thousands of crashes in a computer simulation environment; (3) overlaying the F-CAM generic algorithms onto the simulations of each crash event and observe the kinematic impacts (i.e., benefits) from having initiated warnings and/or automatic braking (including reduction in impact speed, or elimination of the crash). The crash population that could likely benefit from the technologies was identified using nationally representative crash databases. The results from the simulation studies were applied to the national crash population and are presented in terms of crashes avoided, reductions in fatalities, injuries and property damage.