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Evaluation of Connected Vehicle Identification-Aware Mixed Traffic Freeway Cooperative Merging

Haoji Liu, Fatemeh Jahedinia, Zeyu Mu, B. Brian Park

TL;DR

This work addresses safe and efficient freeway merging in mixed traffic by integrating a connected vehicle identification system (VIS) into CAV on-ramp merging. It formulates the problem, employs real-world exiD trajectory data to generate dangerous merging scenarios, and proposes Recursive and Cooperative control modes that depend on VIS availability. A chi-square based VIS mechanism fuses GPS and radar data, with a fixed identification time of 3.5 s, and the framework evaluates safety, energy, and comfort using multiple cases. Results show that VIS enhances safety through better coordination but can constrain cooperation space due to identification delays, while cooperation generally improves energy efficiency and comfort; infrastructure such as RSUs could mitigate VIS drawbacks and extend detection ranges.

Abstract

Cooperative on-ramp merging control for connected automated vehicles (CAVs) has been extensively investigated. However, they did neglect the connected vehicle identification process, which is a must for CAV cooperations. In this paper, we introduced a connected vehicle identification system (VIS) into the on-ramp merging control process for the first time and proposed an evaluation framework to assess the impacts of VIS on on-ramp merging performance. First, the mixed-traffic cooperative merging problem was formulated. Then, a real-world merging trajectory dataset was processed to generate dangerous merging scenarios. Aiming at resolving the potential collision risks in mixed traffic where CAVs and traditional human-driven vehicles (THVs) coexist, we proposed on-ramp merging strategies for CAVs in different mixed traffic situations considering the connected vehicle identification process. The performances were evaluated via simulations. Results indicated that while safety was assured for all cases with CAVs, the cases with VIS had delayed initiation of cooperation, limiting the range of cooperative merging and leading to increased fuel consumption and acceleration variations.

Evaluation of Connected Vehicle Identification-Aware Mixed Traffic Freeway Cooperative Merging

TL;DR

This work addresses safe and efficient freeway merging in mixed traffic by integrating a connected vehicle identification system (VIS) into CAV on-ramp merging. It formulates the problem, employs real-world exiD trajectory data to generate dangerous merging scenarios, and proposes Recursive and Cooperative control modes that depend on VIS availability. A chi-square based VIS mechanism fuses GPS and radar data, with a fixed identification time of 3.5 s, and the framework evaluates safety, energy, and comfort using multiple cases. Results show that VIS enhances safety through better coordination but can constrain cooperation space due to identification delays, while cooperation generally improves energy efficiency and comfort; infrastructure such as RSUs could mitigate VIS drawbacks and extend detection ranges.

Abstract

Cooperative on-ramp merging control for connected automated vehicles (CAVs) has been extensively investigated. However, they did neglect the connected vehicle identification process, which is a must for CAV cooperations. In this paper, we introduced a connected vehicle identification system (VIS) into the on-ramp merging control process for the first time and proposed an evaluation framework to assess the impacts of VIS on on-ramp merging performance. First, the mixed-traffic cooperative merging problem was formulated. Then, a real-world merging trajectory dataset was processed to generate dangerous merging scenarios. Aiming at resolving the potential collision risks in mixed traffic where CAVs and traditional human-driven vehicles (THVs) coexist, we proposed on-ramp merging strategies for CAVs in different mixed traffic situations considering the connected vehicle identification process. The performances were evaluated via simulations. Results indicated that while safety was assured for all cases with CAVs, the cases with VIS had delayed initiation of cooperation, limiting the range of cooperative merging and leading to increased fuel consumption and acceleration variations.
Paper Structure (15 sections, 8 equations, 7 figures, 3 tables)

This paper contains 15 sections, 8 equations, 7 figures, 3 tables.

Table of Contents

  1. INTRODUCTION
  2. PROBLEM DESCRIPTION
  3. Scenario description
  4. Vehicle dynamics and merging constraints of CAVs
  5. Control modes for CAVs
  6. Evaluation Framework
  7. DATA AND METHOD
  8. Real-World Merging Data
  9. Operation Strategies for on-ramp CAVs
  10. Connected vehicle identification system (VIS)
  11. SIMULATION RESULTS AND DISCUSSIONS
  12. Evaluation measures for merging control performance
  13. Merging control results
  14. Discussions
  15. CONCLUSIONS

Figures (7)

  • Figure 1: An example of a vehicle identification failure in the on-ramp merging area and its negative impact. The ego on-ramp CAV fails to identify which vehicle is a CAV and which CAV broadcasts which message, therefore possible bad consequences may happen.
  • Figure 2: The on-ramp merging scenario. From the Start Line, on-ramp CAVs can detect nearby mainline vehicles with good line of sight.
  • Figure 3: Evaluation framework.
  • Figure 4: Two merging parts in the auxiliary lane.
  • Figure 5: GPS-measured and radar-measured distance errors.
  • ...and 2 more figures