skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: Model-based assessment of the impact of driver-assist vehicles using kinetic theory
Abstract In this paper, we consider a kinetic description of follow-the-leader traffic models, which we use to study the effect of vehicle-wise driver-assist control strategies at various scales, from that of the local traffic up to that of the macroscopic stream of vehicles. We provide theoretical evidence of the fact that some typical control strategies, such as the alignment of the speeds and the optimisation of the time headways, impact on the local traffic features (for instance, the speed and headway dispersion responsible for local traffic instabilities) but have virtually no effect on the observable macroscopic traffic trends (for instance, the flux/throughput of vehicles). This unobvious conclusion, which is in very nice agreement with recent field studies on autonomous vehicles, suggests that the kinetic approach may be a valid tool for an organic multiscale investigation and possibly the design of driver-assist algorithms.  more » « less
Award ID(s):
1837481
PAR ID:
10204903
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Zeitschrift für angewandte Mathematik und Physik
Volume:
71
Issue:
5
ISSN:
0044-2275
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Recent studies have leveraged the existence of network macroscopic fundamental diagrams (MFD) to develop regional control strategies for urban traffic networks. Existing MFD-based control strategies focus on vehicle movement within and across regions of an urban network and do not consider how freeway traffic can be controlled to improve overall traffic operations in mixed freeway and urban networks. The purpose of this study is to develop a coordinated traffic management scheme that simultaneously implements perimeter flow control on an urban network and variable speed limits (VSL) on a freeway to reduce total travel time in such a mixed network. By slowing down vehicles traveling along the freeway, VSL can effectively meter traffic exiting the freeway into the urban network. This can be particularly useful since freeways often have large storage capacities and vehicles accumulating on freeways might be less disruptive to overall system operations than on urban streets. VSL can also be used to change where freeway vehicles enter the urban network to benefit the entire system. The combined control strategy is implemented in a model predictive control framework with several realistic constraints, such as gradual reductions in freeway speed limit. Numerical tests suggest that the combined implementation of VSL and perimeter metering control can improve traffic operations compared with perimeter metering alone. 
    more » « less
  2. Driver assist features such as adaptive cruise control (ACC) and highway assistants are becoming increasingly prevalent on commercially available vehicles. These systems are typically designed for safety and rider comfort. However, these systems are often not designed with the quality of the overall traffic flow in mind. For such a system to be beneficial to the traffic flow, it must be string stable and minimize the inter-vehicle spacing to maximize throughput, while still being safe. We propose a methodology to select autonomous driving system parameters that are both safe and string stable using the existing control framework already implemented on commercially available ACC vehicles. Optimal parameter values are selected via model-based optimization for an example highway assistant controller with path planning. 
    more » « less
  3. 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. 
    more » « less
  4. The Intelligent Driver Model (IDM) is one of the widely used car-following models to represent human drivers in mixed traffic simulations. However, the standard IDM performs too well in energy efficiency and comfort (acceleration) compared with real-world human drivers. In addition, many studies assessed the performance of automated vehicles interacting with human-driven vehicles (HVs) in mixed traffic where IDM serves as HVs based on the assumption that the IDM represents an intelligent human driver that performs not better than automated vehicles (AVs). When a commercially available control system of AVs, Adaptive Cruise Control (ACC), is compared with the standard IDM, it is found that the standard IDM generally outperforms ACC in fuel efficiency and comfort, which is not logical in an evaluation of any advanced control logic with mixed traffic. To ensure the IDM reasonably mimics human drivers, a dynamic safe time headway concept is proposed and evaluated. A real-world NGSIM data set is utilized as the human drivers for simulation-based comparisons. The results indicate that the performance of the IDM with dynamic time headway is much closer to human drivers and worse than the ACC system as expected. 
    more » « less
  5. Preceding vehicles typically dominate the movement of following vehicles in traffic systems, thereby significantly influencing the efficacy of eco-driving control that concentrates on vehicle speed optimization. To potentially mitigate the negative effect of preceding vehicles on eco-driving control at the signalized intersection, this study proposes an overtaking-enabled eco-approach control (OEAC) strategy. It combines driving lane planning and speed optimization for connected and automated vehicles to relax the first-in-first-out queuing policy at the signalized intersection, minimizing the host vehicle’s energy consumption and travel delay. The OEAC adopts a two-stage receding horizon control framework to derive optimal driving trajectories for adapting to dynamic traffic conditions. In the first stage, the driving lane optimization problem is formulated as a Markov decision process and solved using dynamic programming, which takes into account the uncertain disturbance from preceding vehicles. In the second stage, the vehicle’s speed trajectory with the minimal driving cost is optimized rapidly using Pontryagin’s minimum principle to obtain the closed-form analytical optimal solution. Extensive simulations are conducted to evaluate the effectiveness of the OEAC. The results show that the OEAC is excellent in driving cost reduction over constant speed and regular eco-approach and departure strategies in various traffic scenarios, with an average improvement of 20.91% and 5.62%, respectively. 
    more » « less