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... radar system consisted of three Eaton Vorad EVT- 300 radar units. One radar unit was installed in the front, facing forward, and the other two units were installed at the back of the vehicle facing rearward and offset by 6 degrees from the longitudinal axis ( Figure 4). These radars were configured so distance information t o surrounding vehicles could be collected. ...
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... Trajectory data are obtained using the video recordings [38,[79][80] and naturalistic studies [81]. FHWA'S Next Generation Simulation project [82] shared several datasets of vehicle trajectories collected on expressway and urban arterials in US. ...
Most published microscopic driving behavior models, such as car following and lane changing, were developed for homogeneous and lane-based settings. In the emerging and developing world, traffic is characterized by a wide mix of vehicle types (e.g., motorized and non-motorized, two, three and four wheelers) that differ substantially in their dimensions, performance capabilities and driver behavior and by a lack of lane discipline. This paper presents a review of current driving behavior models in the context of mixed traffic, discusses their limitations and the data and modeling challenges that need to be met in order to extend and improve their fidelity. The models discussed include those for longitudinal and lateral movements and gap acceptance. The review points out some of the limitations of current models. A main limitation of current models is that they have not explicitly considered the wider range of situations that drivers in mixed traffic may face compared to drivers in homogeneous lane-based traffic, and the strategies that they may choose in order to tackle these situations. In longitudinal movement, for example, such strategies include not only strict following, but also staggered following, following between two vehicles and squeezing. Furthermore, due to limited availability of trajectory data in mixed traffic, most of the models are not estimated rigorously. The outline of modeling framework for integrated driver behavior was discussed finally.
... The method used in this data collection effort was previously detailed elsewhere (15,(17)(18)(19) and is briefly reviewed here. ...
... These changes accounted for 37.2% of the total and constituted the single largest type. A subset of 500 maneuvers was then selected for in-depth analysis with a customized data integration program (15,17,19). This process included eye glance analysis performed manually frame by frame (sampled at 30 frames per second and analyzed at 10 frames per second). ...
... The requirement of an elevated position is also a limitation of this method (c) Floating-car methodThe advantage of the floating-car method is that the data processing is simpler and it can directly collect the useful parameters, depending on the sensors employed. The floating car method can be equipped with a wide range of sensors, including camcorders (for example, as used byOlsen and Wierwille [2001]). Floating-car method has some limitations because the data can only be collected from a limited number of instrumented vehicles. ...
This paper explains the modelling of emission in real world onboard measurement under local driving condition for engine size 1000cc and 600cc for motorcycles in Edinburgh. Impact of instantaneous speed, acceleration on emission have been investigated on the air quality management area (AQMA) in Edinburgh. Emission directly observed from the analyser have been converted from ppm and % unit into gm/sec by using the fuel consumption estimates and carbon mass balance equation Finally average emission factors for CO, HC, and NOX along the corridor have been estimated on time based (gm per second) and distance based (gm/km). Since emissions are primarily affected by speed, therefore a correlation between emission factors and speed have been developed. Onboard emission measurements have advantages to collect the emission data into different driving cycle i.e. vehicle operating modes (idling cruise, acceleration, and deceleration). This has been further investigated by developing the relationship between time spent in these modes and emission. These types of models are suitable, in sustainable development of transportation system, traffic demand management, signal coordination, and environment friendly application for Intelligent Transportation System (ITS).
... The method used in this data collection effort was previously detailed elsewhere (15,(17)(18)(19) and is briefly reviewed here. ...
... These changes accounted for 37.2% of the total and constituted the single largest type. A subset of 500 maneuvers was then selected for in-depth analysis with a customized data integration program (15,17,19). This process included eye glance analysis performed manually frame by frame (sampled at 30 frames per second and analyzed at 10 frames per second). ...
Understanding drivers’ eye behavior prior to lane changing is an important element of designing usable, safe lane-change collision avoidance systems (LCAS) that will fit well with drivers’ expectations. This understanding could potentially lead to developments that could be applied to LCAS as well as a variety of collision avoidance systems (CAS).
This paper presents findings regarding driver eye glance behaviors, comparing lanechange maneuvers to straight-ahead (baseline) driving events. Specific eye glance patterns prior to lane-change initiation were observed. When preparing to make a lane change to the left as compared to driving straight ahead, drivers doubled the number of glances toward the rear view mirror, and were much more likely to look at other locations associated with moving to the left including left mirror and blind spot.
Based on the eye glance patterns observed and previous results the following is recommended: 1) use visual presence detection indicator displays to provide information about vehicles in the rear adjacent lane anytime a vehicle is detected; 2) present a presence indicator in a visual format; 3) consider the left mirror and rearview mirror locations for providing lanechange information to the driver.
The process of acquiring and analyzing eye glance movements is well worth the investment in resources. Understanding eye glance movements are important in the development collision warning systems that fit in with drivers’ natural glance patterns. However, prototype systems must be developed and tested before implementation, and the exact location and format of warning systems warrant a separate research and development effort to assure safety and reliability.
... Individual differences among drivers and the directionality of lane change can have a significant impact on mirror sampling behavior. Recent research (9)(10)(11)(12) has expanded an understanding of driver behavior by examining driver behavior before and during naturalistic lane changes. ...
Data are presented on the eye glance behavior of passenger car and van drivers before the start of discretionary lane changes. Thirty-nine volunteers ranging from 20 to 60 years of age served as either van drivers (N = 19) or passenger car drivers (N = 20) in the study. Each driver used an instrumented vehicle and was accompanied by a ride-along observer in daylight and dry pavement conditions. The test route included driving on both public highways at 55 mph or more and city roads at 25 to 35 mph. A total of 549 lane changes (290 for vans, 259 for passenger cars) were analyzed in terms of driver eye glance behavior 10 s before the lane change start. Results indicated that for left-to-right lane changes, the probability of a glance to the center mirror was substantially higher than the probability of a glance to the right side mirror. For right-to-left lane changes, the probability of a glance to the center mirror was substantially less than that for rightward lane changes, and the probability of a glance to the left side mirror was appreciably higher than that for right side mirror use in rightward lane changes. These results held for both van and passenger car drivers. Except for a slightly higher probability of over-the-shoulder glances on city roads, these results hold for both highway and city street driving. These data should be factored into the design of lane change warning system displays and mirror systems.
... Individual differences among drivers and the directionality of lane change can have a significant impact on mirror sampling behavior. Recent research (9)(10)(11)(12) has expanded an understanding of driver behavior by examining driver behavior before and during naturalistic lane changes. ...
Data are presented on the eye glance behavior of passenger car and van drivers before the start of discretionary lane changes. Thirty-nine volunteers ranging from 20 to 60 years of age served as either van drivers (N = 19) or passenger car drivers (N = 20) in the study. Each driver used an instrumented vehicle and was accompanied by a ride-along observer in daylight and dry pavement conditions. The test route included driving on both public highways at 55 mph or more and city roads at 25 to 35 mph. A total of 549 lane changes (290 for vans, 259 for passenger cars) were analyzed in terms of driver eye glance behavior 10 s before the lane change start. Results indicated that for left-to-right lane changes, the probability of a glance to the center mirror was substantially higher than the probability of a glance to the right side mirror. For right-to-left lane changes, the probability of a glance to the center mirror was substantially less than that for rightward lane changes, and the probability of a glance to the left side mirror was appreciably higher than that for right side mirror use in rightward lane changes. These results held for both van and passenger car drivers. Except for a slightly higher probability of over-the-shoulder glances on city roads, these results hold for both highway and city street driving. These data should be factored into the design of lane change warning system displays and mirror systems.
... The method used in this data collection effort was previously detailed elsewhere (15,(17)(18)(19) and is briefly reviewed here. ...
... These changes accounted for 37.2% of the total and constituted the single largest type. A subset of 500 maneuvers was then selected for in-depth analysis with a customized data integration program (15,17,19). This process included eye glance analysis performed manually frame by frame (sampled at 30 frames per second and analyzed at 10 frames per second). ...
Understanding drivers’ eye behavior before lane changing is an important aspect of designing usable, safe lane-change collision-avoidance systems (LCAS) that will fit well with drivers’ expectations. This understanding could lead to improvements for LCAS as well as for a variety of other collision avoidance systems. Findings regarding driver eye glance behaviors are presented in a comparison of lane change maneuvers with straight-ahead (baseline) driving events. Specific eye glance patterns before lane change initiation were observed. When preparing to make a lane change to the left as compared with driving straight ahead, drivers doubled the number of glances toward the rearview mirror and were much more likely to look at other locations associated with moving to the left, including the left mirror and blind spot. On the basis of the eye glance patterns observed and previous results, the following recommendations are made: (a) visual presence detection indicator displays should be used to provide information about vehicles in the rear adjacent lane any time a vehicle is detected, (b) a presence indicator should be presented in a visual format, and (c) the left mirror and rearview mirror locations should be considered for providing lane change information to the driver. The process of acquiring and analyzing eye glance movements is well worth the investment in resources. However, prototype systems must be tested before implementation, and the exact location and format of warning systems warrant a separate research and development effort to ensure safety and reliability.
... The method has been described previously (Lee, Olsen, & Wierwille, 2003;Olsen, 2003;Olsen & Wierwille, 2001). Sixteen commuters were recruited to drive two instrumented vehicles to and from work, with no experimenter present, for 20 business days. ...
Five design guidelines were created to aid in the development of lane change crash avoidance systems (LCAS), using on-road data. In preparation for guideline development, field data were used to characterize a sample of lane changes and baseline (straight-ahead) driving events. Analysis of lane change frequency and duration, as well as turn signal use and eye glance movements revealed unique patterns among drivers and conditions. The "vehicle + signal" predictive logistic regression model is recommended, taking advantage of distance, time-to-collision (TTC), and turn signal data. Design guidelines as the basis for LCAS development were: 1) Warning levels should include presence detection and imminent warnings; 2) Display modality should be visual displays for presence detection and auditory/tactile displays for imminent warnings; 3) Display location (visual) should include forward and mirror locations; 4) Predictive warning algorithms should include TTC, distance, brake and turn signal use, eye behavior, lane position, side object information, and acceleration; 5) In terms of system integration, LCAS should be designed in the context of other in-vehicle systems to maximize effectiveness and safety. Prior to implementation, testing is strongly recommended regarding warning levels, display location, warning format, predictive algorithms, and alarm rates.
... An elaborate report on the benefits of CAS by NHTSA Benefits Group presents detailed analysis to estimate the impact of crash avoidance systems by using the best available estimates of system and driver performance. In addition, many studies were conducted to appraise the technical aspects of the issue, e.g., Martin et al. [2], Olsen and Wierwille [3], Burgett and Gunderson [4], Burgett and Miller [5], Martin and Burgett [6]. These studies mainly focus on the technical aspects of the crash avoidance issue. ...
A crash can be thought of as a system composed of several elements, including drivers and vehicles that continually interact with each other, while a crash database is a record of the errors attributable to different components of the crash system. Learning from mistakes (errors) is important if crashes are to be avoided. With more than one hundred variables related to the drivers, occupants, crash sites and vehicles involved in crashes, the General Estimates System database contains crucial information about the phenomena of crash occurrence. This information can be used to develop crash countermeasures at all levels, including drivers, vehicles and roadways. One of the ways to achieve this objective is to explore the data for any patterns that exist among drivers, vehicles, and roadways. In this study, we identify driver and vehicle characteristics that contributed to their crash involvement. Preliminary analysis was conducted for selection of crash variables that were relevant to drivers and vehicles involvement in crashes. One of the data mining techniques called "principal components analysis" was further used to identify age-and gender-based groups of drivers and body types of vehicles by highlighting their relation with the crash variables. Some of the variables that were considered in this study included distraction, drinking, speeding etc. (at driver level), and vehicle contributing factors, vehicle's control and the path prior to its initial involvement in the crash (at vehicle level). This in turn helped in identifying the hidden characteristics that may have adversely influenced the driving behavior of drivers and/or running of vehicles, eventually resulting in crashes.
Driving behavior analysis has diverse applications in intelligent transportation systems (ITSs). The naturalistic driving data potentially contain rich information regarding human drivers' habits and skills in practical and natural driving conditions. But mining knowledge from them is challenging. In this paper, we propose a novel approach for analyzing driving stability using naturalistic driving data. Our method can extract features, based on the random matrix theory, to reflect the statistical difference between actual driving data and the data that would be generated by a theoretically ideal driver, and thus imply the skillful level of a driver in terms of vehicle control in both longitudinal and lateral directions. The execution of our method on a practical ITS dataset is conducted. Using the extracted features, a driving behavior analysis application that partitions drivers into clusters to identify common driving stability characteristics is demonstrated and discussed.
