Validating a Traffic Conflict Prediction Technique for Motorways Using a Simulation Approach

Abstract

With the ever-increasing advancements in the technology of driver assistant systems, there is a need for a comprehensive way to identify traffic conflicts to avoid collisions. Although significant research efforts have been devoted to traffic conflict techniques applied for junctions, there is dearth of research on these methods for motorways. This paper presents the validation of a traffic conflict prediction algorithm applied to a motorway scenario in a simulated environment. An automatic video analysis system was developed to identify lane change and rear-end conflicts as ground truth. Using these conflicts, the prediction ability of the traffic conflict technique was validated in an integrated simulation framework. This framework consisted of a sub-microscopic simulator, which provided an appropriate testbed to accurately simulate the components of an intelligent vehicle, and a microscopic traffic simulator able to generate the surrounding traffic. Results from this framework show that for a 10% false alarm rate, approximately 80% and 73% of rear-end and lane change conflicts were accurately predicted, respectively. Despite the fact that the algorithm was not trained using the virtual data, the sensitivity was high. This highlights the transferability of the algorithm to similar road networks, providing a benchmark for the identification of traffic conflict and a relevant step for developing safety management strategies for autonomous vehicles.

Examining the feasibility of current spiral curve design controls for LiDAR‐based automated vehicles

“…With the collected pairs of simulated and field‐tested ASD results, the Geoffrey E. Havers (GEH) statistic given by Equation (2) [73] was employed. $$$$\begin{equation}{\rm{GEH}} = \sqrt {\frac{{2{{\left( {{\rm{AS}}{{\rm{D}}_{\rm{s}}} - {\rm{AS}}{{\rm{D}}_{\rm{f}}}} \right)}^2}}}{{{\rm{AS}}{{\rm{D}}_{\rm{s}}} + {\rm{AS}}{{\rm{D}}_{\rm{f}}}}}} \end{equation}$$$$where ASD s and ASD f are the simulated and field‐tested ASD values, respectively, given the same condition.…”

“…With the collected pairs of simulated and field-tested ASD results, the Geoffrey E. Havers (GEH) statistic given by Equation (2) [73] was employed. where ASD s and ASD f are the simulated and field-tested ASD values, respectively, given the same condition.…”

A New Modeling Approach for Predicting Vehicle-Based Safety Threats

“…To foster proactive vehicle-based safety, a traffic conflict is utilised as a measure of crash nearness [26]. Traffic conflicts are characterised by a set of indicators; a spatio-temporal proximity indicator and an evasive action response that takes place so as to avoid a potential accident [27], [28].…”

“…These conflicts were manually validated and further validated by developed equations. The readers are referred to Formosa et al [17], [28] for a more thorough description of the traffic conflict extraction.…”

Evaluating the Impact of Lane Marking Quality on the Operation of Autonomous Vehicles

“…The outcomes of such simulations can be used to prevent the same disengagement reoccurrence and modify existing infrastructure to prevent further disengagements. A sub-microscopic traffic simulator environment (e.g., PreScan (Formosa et al, 2022)) could provide an appropriate testbed to accurately represent the AV functionality, communication, mixed fleets and varying road, weather and light conditions. To obtain more realistic values, the surrounding dynamic traffic can be generated via a microscopic simulator (e.g.…”