The objective of this research was to determine what influence geometric design elements of roadway may have on driver behavior during the overtaking maneuver. This was part of a larger research effort to eliminate cr...The objective of this research was to determine what influence geometric design elements of roadway may have on driver behavior during the overtaking maneuver. This was part of a larger research effort to eliminate crashes （and the resulting fatalities and injuries） between bicycles and motorized vehicles. The data collection process produced 1151 observations with approximately 40 different independent variables for each data point through direct observation, sensor logging, or derivation from other independent variables. Prior research by the authors developed a means to collect real-time field data through the use of a bicycle-mounted data collection system. The collected data was then used to model lateral clearance distance be- tween vehicles and bicycles. The developed model confirmed field observations that the lateral clearance distance provided by drivers changes with vehicle speed and oncoming vehicle presence. These observations were presented by the authors previously. The model shows that driver behavior can be adjusted by the inclusion, or exclusion, of geometric elements. Evaluating roadways （or roadway designs） based on this model will enable stakeholders to identify those roadway segments where a paved shoulder would prove an effective safety countermeasure. This research will also enable roadway designers to better identify during the design phase those roadway segments that should be constructed with a paved shoulder.展开更多
Traffic on Indian roads is highly mixed in nature with wide variations in the static and dynamic characteristics of vehicles. These vehicles do not follow strict lane discipline and occupy any available lateral positi...Traffic on Indian roads is highly mixed in nature with wide variations in the static and dynamic characteristics of vehicles. These vehicles do not follow strict lane discipline and occupy any available lateral position on the road space. Overtaking is one of the most complex and important manoeuvre on undivided roads where the vehicles use the opposing lane to overtake the slower vehicles with the presence of oncoming vehicles from opposite direction. They are unavoidable especially in the case of mixed traffic conditions where there is always a speed difference between the fast and slow moving vehicles. Overtaking process involves lane-changing manoeuvres, acceleration and deceleration actions and estimation of relative speed of overtaking and overtaken vehicles, and also, estimation of speed and distance of the oncoming vehicle. The main objective of the present study is to study the overtaking characteristics of vehicles on undivided roads under mixed traffic conditions. For this purpose, details of overtaking data were collected on a two-lane two-way undivided road using moving car observer method and registration plate method. The overtaking characteristics of all types of vehicles under mixed traffic conditions were observed and mathematically modelled. The data extracted and analysed were the acceleration characteristics, speeds of the overtaking vehicles, overtaking time, overtaking distances, safe opposing gap required for overtaking, flow rates, overtaking frequencies, types of overtaking strategy, and types of overtaking and overtaken vehicles. Two types of overtaking strategies were observed in the field such as flying overtaking and accelerative overtaking. Graphs were plotted between the relative speed of the overtaking and overtaken vehicles against the overtaking time and negative correlation was found between the speed differential and total overtaking time for all categories of vehicles. It was observed that the number of overtaking increases with increase in the flow rate in the on-going dire展开更多
As intelligent vehicles usually have complex overtaking process,a safe and efficient automated overtaking system(AOS)is vital to avoid accidents caused by wrong operation of drivers.Existing AOSs rarely consider longi...As intelligent vehicles usually have complex overtaking process,a safe and efficient automated overtaking system(AOS)is vital to avoid accidents caused by wrong operation of drivers.Existing AOSs rarely consider longitudinal reactions of the overtaken vehicle(OV)during overtaking.This paper proposed a novel AOS based on hierarchical reinforcement learning,where the longitudinal reaction is given by a data-driven social preference estimation.This AOS incorporates two modules that can function in different overtaking phases.The first module based on semi-Markov decision process and motion primitives is built for motion planning and control.The second module based on Markov decision process is designed to enable vehicles to make proper decisions according to the social preference of OV.Based on realistic overtaking data,the proposed AOS and its modules are verified experimentally.The results of the tests show that the proposed AOS can realize safe and effective overtaking in scenes built by realistic data,and has the ability to flexibly adjust lateral driving behavior and lane changing position when the OVs have different social preferences.展开更多
基金support of this study from the Traffic Operations and Safety(TOPS) Laboratory at the University of Wisconsin-Madison
文摘The objective of this research was to determine what influence geometric design elements of roadway may have on driver behavior during the overtaking maneuver. This was part of a larger research effort to eliminate crashes （and the resulting fatalities and injuries） between bicycles and motorized vehicles. The data collection process produced 1151 observations with approximately 40 different independent variables for each data point through direct observation, sensor logging, or derivation from other independent variables. Prior research by the authors developed a means to collect real-time field data through the use of a bicycle-mounted data collection system. The collected data was then used to model lateral clearance distance be- tween vehicles and bicycles. The developed model confirmed field observations that the lateral clearance distance provided by drivers changes with vehicle speed and oncoming vehicle presence. These observations were presented by the authors previously. The model shows that driver behavior can be adjusted by the inclusion, or exclusion, of geometric elements. Evaluating roadways （or roadway designs） based on this model will enable stakeholders to identify those roadway segments where a paved shoulder would prove an effective safety countermeasure. This research will also enable roadway designers to better identify during the design phase those roadway segments that should be constructed with a paved shoulder.
文摘Traffic on Indian roads is highly mixed in nature with wide variations in the static and dynamic characteristics of vehicles. These vehicles do not follow strict lane discipline and occupy any available lateral position on the road space. Overtaking is one of the most complex and important manoeuvre on undivided roads where the vehicles use the opposing lane to overtake the slower vehicles with the presence of oncoming vehicles from opposite direction. They are unavoidable especially in the case of mixed traffic conditions where there is always a speed difference between the fast and slow moving vehicles. Overtaking process involves lane-changing manoeuvres, acceleration and deceleration actions and estimation of relative speed of overtaking and overtaken vehicles, and also, estimation of speed and distance of the oncoming vehicle. The main objective of the present study is to study the overtaking characteristics of vehicles on undivided roads under mixed traffic conditions. For this purpose, details of overtaking data were collected on a two-lane two-way undivided road using moving car observer method and registration plate method. The overtaking characteristics of all types of vehicles under mixed traffic conditions were observed and mathematically modelled. The data extracted and analysed were the acceleration characteristics, speeds of the overtaking vehicles, overtaking time, overtaking distances, safe opposing gap required for overtaking, flow rates, overtaking frequencies, types of overtaking strategy, and types of overtaking and overtaken vehicles. Two types of overtaking strategies were observed in the field such as flying overtaking and accelerative overtaking. Graphs were plotted between the relative speed of the overtaking and overtaken vehicles against the overtaking time and negative correlation was found between the speed differential and total overtaking time for all categories of vehicles. It was observed that the number of overtaking increases with increase in the flow rate in the on-going dire
基金The authors would like to appreciate the financial support of the National Natural Science Foundation of China(Grant No.61703041)the technological innovation program of Beijing Institute of Technology(2021CX11006).
文摘As intelligent vehicles usually have complex overtaking process,a safe and efficient automated overtaking system(AOS)is vital to avoid accidents caused by wrong operation of drivers.Existing AOSs rarely consider longitudinal reactions of the overtaken vehicle(OV)during overtaking.This paper proposed a novel AOS based on hierarchical reinforcement learning,where the longitudinal reaction is given by a data-driven social preference estimation.This AOS incorporates two modules that can function in different overtaking phases.The first module based on semi-Markov decision process and motion primitives is built for motion planning and control.The second module based on Markov decision process is designed to enable vehicles to make proper decisions according to the social preference of OV.Based on realistic overtaking data,the proposed AOS and its modules are verified experimentally.The results of the tests show that the proposed AOS can realize safe and effective overtaking in scenes built by realistic data,and has the ability to flexibly adjust lateral driving behavior and lane changing position when the OVs have different social preferences.
