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Accurate prediction of traffic flow dynamics is a key step towards effective congestion mitigation strategies. The dynamic nature of traffic flow and lack of comprehensive data coverage (e.g., availability of data at loop detector locations), however, have historically prevented accurate traffic state prediction, leading to the widespread utilization of reactive congestion mitigation strategies. The introduction of connected automated vehicles provides an opportunity to address this challenge. These vehicles rely on trajectory-level prediction of their surrounding traffic environment to plan a safe and efficient path. This study proposes a methodology to utilize the outcome of such predictions to estimate the future traffic state. Moreover, the same approach can be applied to data from connected vehicles for traffic state prediction. Since in many driving scenarios, more than one maneuver is feasible, it is more logical to predict the location of the vehicles in a probabilistic manner based on the probability of different maneuvers. The key contribution of this study is to introduce a methodology to convert such probabilistic trajectory predictions to aggregate traffic state predictions (i.e., flow, space–mean speed, and density). The key advantage of this approach (over directly predicting traffic state based on aggregated traffic data) is its ability to capture the interactions among vehicles to increase the accuracy of the prediction. The down side of this approach, on the other hand, is that any increase in the prediction horizon reduces the accuracy of prediction (due to the uncertainty in the vehicles’ interactions and the increase in the possibility of different maneuvers). At the microscopic level, this study proposes a probability based version of the time–space diagram, and at the macroscopic level, this study proposes probabilistic estimates of flow, density, and space–mean speed using the trajectory-level predictions. To evaluate the effectiveness of the proposed approach in predicting traffic state, the mean absolute percentage error for each probabilistic macroscopic estimate is evaluated on multiple subsamples of the NGSIM US-101 and I-80 data sets. Moreover, while introducing this novel traffic state prediction approach, this study shows that the fundamental relation among the average traffic flow, density, and space–mean speed is still valid under the probabilistic formulations of this study.
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Exploiting Learning and Scenario-Based Specification Languages for the Verification and Validation of Highly Automated Driving
We propose a series of methods based on learning key structural properties from traffic data-basis and on statistical model checking, ultimately leading to the construction of a scenario catalogue capturing requirements for controlling criticality for highly autonomous vehicles. We sketch underlying mathematical foundations which allow to derive formal confidence levels that vehicles tested by such a scenario catalogue will maintain the required control of criticality in real traffic matching the probability distributions of key parameters of data recorded in the reference data base employed for this process.
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- Award ID(s):
- 1743772
- PAR ID:
- 10076294
- Date Published:
- Journal Name:
- 2018 IEEE/ACM 1st International Workshop on Software Engineering for AI in Autonomous Systems (SEFAIAS)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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