Browsing by Author "Grove, Kevin"
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- Camera-based Feature Identification for EasyMile OperationSarkar, Abhijit; Sundharam, Vaibhav; Manke, Aditi; Grove, Kevin (National Surface Transportation Safety Center for Excellence, 2022-11-15)The EasyMile deployment studied in this work included cameras that captured the 360 degrees of roadway environment around the vehicle. We developed a scene perception algorithm using computer vision technology to track other roadway agents like cars, pedestrians, and bicyclists around the EasyMile LSAV. We used object detection and tracking algorithms to track the trajectories of each of the roadway agents. Then we used perspective geometry and camera specifications to find the relative distances and speeds of these agents with respect to the EasyMile. This helped us understand the configurations of the traffic around the LSAV and study other drivers’ temporal behavior. For example, the collected data shows the approach of any vehicle towards the EasyMile. Finally, we used this information to study other vehicles’ maneuvers and show how the information from the cameras can be used to study simple maneuvers of other vehicles such as cut-ins, lane changes, and following behavior. Through these camera-based tools, we have demonstrated examples from the real-world deployment. We studied following behavior characteristics that show the relative distance and speed of other vehicles’ following behavior. We have also demonstrated cut-in behaviors through the longitudinal and lateral trajectories of cut-in vehicles. We also showed how abrupt cut-ins may lead the EasyMile to apply its brakes, leading to safety critical events for following vehicles. Finally, we demonstrated how pedestrian behavior can be studied via these camera-based methods.
- Checklist for Expert Evaluation of HMIs of Automated Vehicles—Discussions on Its Value and Adaptions of the Method within an Expert WorkshopSchömig, Nadja; Wiedemann, Katharina; Hergeth, Sebastian; Forster, Yannick; Muttart, Jeffrey; Eriksson, Alexander; Mitropoulos-Rundus, David; Grove, Kevin; Krems, Josef; Keinath, Andreas; Neukum, Alexandra; Naujoks, Frederik (MDPI, 2020-04-24)Within a workshop on evaluation methods for automated vehicles (AVs) at the Driving Assessment 2019 symposium in Santa Fe; New Mexico, a heuristic evaluation methodology that aims at supporting the development of human–machine interfaces (HMIs) for AVs was presented. The goal of the workshop was to bring together members of the human factors community to discuss the method and to further promote the development of HMI guidelines and assessment methods for the design of HMIs of automated driving systems (ADSs). The workshop included hands-on experience of rented series production partially automated vehicles, the application of the heuristic assessment method using a checklist, and intensive discussions about possible revisions of the checklist and the method itself. The aim of the paper is to summarize the results of the workshop, which will be used to further improve the checklist method and make the process available to the scientific community. The participants all had previous experience in HMI design of driver assistance systems, as well as development and evaluation methods. They brought valuable ideas into the discussion with regard to the overall value of the tool against the background of the intended application, concrete improvements of the checklist (e.g., categorization of items; checklist items that are currently perceived as missing or redundant in the checklist), when in the design process the tool should be applied, and improvements for the usability of the checklist.
- Driver Visual Behavior While Using Adaptive Cruise Control on Commercial Motor VehiclesGrove, Kevin; Soccolich, Susan A.; Hanowski, Richard J. (National Surface Transportation Safety Center for Excellence, 2019-03-25)This study compared whether commercial motor vehicle drivers spent less time looking at the roadway while cruise control was engaged. The trucks in the study were equipped with commercially available systems that provide adaptive cruise control (ACC), which uses radar to regulate headway in addition to speed when following a lead vehicle. Three metrics were analyzed to assess drivers’ eye-glance behavior during periods of traditional cruise control usage, full ACC usage, and manual car-following: total eyes-off-road time (TEORT), durations of glances off-road, and number of glances off-road. Drivers were observed to spend less time looking at the forward roadway when cruise control was engaged. Drivers were observed to spend less time looking at the roadway when ACC was engaged compared to when manually following a lead vehicle. This difference appears to be due to the truck drivers taking longer glances away from the roadway rather than taking more frequent glances away from the roadway. These differences are important for system designers to consider, as drivers are expected to maintain their attention on the roadway while using driver assistance technologies.
- Evaluation of Package Delivery Truck Drivers: Task Analysis and Development/Validation of an Objective Visual Behavior Measure to Assess PerformanceGrove, Kevin (Virginia Tech, 2008-04-24)The job of a package delivery driver (PDD) is complex and demanding. These drivers must possess many skills in order to succeed in their work, including physical stamina, appropriate decision-making, positive customer interaction, and most importantly, operational safety. Companies must use significant resources, not only to provide insurance for existing drivers, but also to train new drivers to use their visual attention effectively while driving, and companies have a vested interest in ensuring that the most capable trainees are selected for jobs. Currently, subjective assessments of supervisors or managers are typically used to make these determinations. While these are valuable methods for assessing drivers, an objective measure of how well the driver is using his/her visual attention would both assist evaluators in making judgments, as well as make those judgments more accurate. The purpose of the study described herein was to 1) conduct a task analysis of the driving component of the PDD job responsibilities, and 2) create and test an objective measure that a package delivery company could use to evaluate the performance of its drivers. A detailed task analysis based on numerous observations of drivers in their normal work routines was conducted for this research in order to understand these complex tasks. A framework was created for understanding this system of tasks, which was then used to organize all tasks that drivers were observed to perform into more general, goal-oriented activities. Using this task analysis, incidents were identified that were observed while drivers were behind the wheel. This information demonstrated that breakdowns were occurring within the tasks drivers were performing and that improved methods of training and evaluations may be needed as a result. A construct of visual behavior called Head Down Time (HTD) was then created and tested. An individual HDT is defined as the sum of time of all eye gazes away from the primary display (i.e. windshield) between two distinct eye gazes at the primary display while the vehicle is in motion. HDT was evaluated for its ability to differentiate levels of experience between drivers, its relationship to types of route on which drivers delivered, and its relationship to the driving-related incidents that were observed. HDTs were shown to be differed significantly between drivers of low and high experience, with experienced drivers displaying shorter durations of HDT when compared to inexperienced drivers. HDTs also differed in duration when analyzed by the type of route upon which drivers operated. Commercial and urban routes, while not significantly different with respect to HDT, were shown to have increased HDT durations when compared to rural routes and, in turn, residential routes were found to have significantly longer HDTs than did rural routes and may have significantly shorter durations compared to commercial and urban. Finally, HDTs that were associated with observed driving incidents in terms of chronological proximity were shown to be of significantly longer duration than were HDTs that were not associated with incidents. All tests were conducted using appropriate statistical measures, including t-tests at a level of α = 0.05 for each dataset. Applications of this research include: 1) improvement of PDD training and evaluation methods through use of a detailed task analysis, 2) improvement in how package delivery companies define incidents and train PDD toward the prevention of incidents based on task analysis and observations as to incident frequency, and 3) the further development of HDT as a possible objective measure to supplement the training and evaluation of PDD.
- Impact of Highly Automated Vehicle (L4/5 AV) External Communication on Other Road User BehaviorsRossi-Alvarez, Alexandria I.; Grove, Kevin; Klauer, Charlie; Miles, Melissa; Schaudt, Andy; Doerzaph, Zachary R. (SAFE-D: Safety Through Disruption National University Transportation Center, 2022-10)The advancement of SAE Level 4+ Automated Vehicles (L4/5 AVs) has led numerous stakeholders to develop external communication systems for these vehicles. Most research on vehicles emulating these displays has been conducted using one vehicle. However, it is vital to understand how communication to vulnerable road users (VRUs) is affected when multiple L4/5 vehicles are present. This study examined how L4/5 AVs can best communicate their intentions (e.g., turning, stopping, yielding) to VRUs and drivers of conventional vehicles. Subjective and objective data was collected to assess road user responses to two vehicles emulating L4/5 displays, from both a passenger and pedestrian perspective. Participants with no prior knowledge of the experiment’s design or intent experienced three light patterns that provided information regarding L4/5 AVs’ intent to slow/stop, begin, and travel with simulated automation active. Overall, participants were overwhelmed by multiple vehicles with different light bars in their crossing vicinity and found it difficult to prioritize attention. These results have implications for future design of external communication displays on L4/5 AVs. Training may be necessary for road users, given the relatively low percentage of participants who understood the meaning of these displays after multiple exposures and participants’ confusion in where to look and how to interpret the intention of the displays when multiple vehicles were present.
- Investigating Drivers’ Compensatory Behavior when Using a Mobile DeviceFitch, Gregory M.; Toole, Laura; Grove, Kevin; Soccolich, Susan A.; Hanowski, Richard J. (National Surface Transportation Safety Center for Excellence, 2017-01-12)The purpose of this study was to investigate driver performance and risk associated with mobile device use (MDU) from previously collected naturalistic driving data. There were two primary objectives: (1) to investigate commercial motor vehicle (CMV) driver adaptation when conversing on a cell phone; and (2) to investigate the relationship between drowsiness and the safety-critical event (SCE) risk associated with MDU. The first goal was to investigate whether CMV and light vehicle (LV) drivers alter the way they drive when conversing on a cell phone. It was hypothesized that drivers may increase their safety margin when conversing on a mobile device by slowing down and increasing their headway to a lead vehicle, thus compensating for the increased workload. Analysis addressing the first goal provided no indication that CMV or LV drivers increased their longitudinal safety margins when conversing on a cell phone. CMV drivers’ headway to a lead vehicle did not differ despite the fact that they significantly increased their speed by 4 km/h when conversing on a cell phone. However, CMV drivers changed lanes significantly less when conversing on a handheld cell phone. These changes suggest that CMV drivers slightly reduced the driving demands when conversing on a cell phone. The second goal was to investigate the relationship between drowsiness and the SCE risk associated with MDU in CMV drivers. Research has shown that drivers become more alert when conversing on a mobile device (Jellentrup, Metz, & Rothe, 2011). It was thus hypothesized that CMV drivers were at a decreased risk of an SCE when conversing on a hands-free cell phone because the conversation served to stave off drowsiness. The CMV NDS data set used in Olson et al. (2009) was analyzed to address the second goal. Drivers’ driving time and time on duty were used to assess their fatigue level, while the time of day and the amount of sleep they obtained in the previous 24 hours (measured via actigraphy) were used to indirectly assess their drowsiness level. Odds ratios computed the SCE risk for MDU subtasks across binned levels of fatigue and drowsiness. Generalized linear mixed models and chi-squared tests were used to assess changes in MDU frequency across bins. It was found that there was an increase in SCE risk for visual-manual subtasks for all bins in which analyses were possible. CMV drivers had a higher proportion of MDU from 2:00 a.m. to 3:59 a.m. (circadian low period) than for the other times of day that were analyzed. Overall, the research shows that LV and CMV drivers did not increase their longitudinal safety margins when talking on a cell phone. However, it was found that both groups of drivers looked forward more frequently when conversing on a cell phone. This study also found that CMV drivers used their cell phones more frequently at times when they would be drowsy. The increased visual attention to the road as well as the increased use during the early hours of the morning may be reasons why some studies have found that conversing on a cell phone was not associated with an increased SCE risk.