Traffic Flow Management of Autonomous Vehicles Using Platooning and Collision Avoidance Strategies
Abstract
:1. Introduction
- At the first stage, our algorithm resolves congestion on the intersection and creates platoons of vehicles and then reroutes those platoons to alternate paths to minimize the traffic jams. The algorithm also reduces the intra-platoon spacing while increasing the lane capacity.
- The proposed algorithm also resolves the congestion for the traffic coming behind the intersection by notifying them in advance about the upcoming congestion and rerouting them to alternate routes for the sake of congestion avoidance.
- We propose strategies to mitigate exceptions that occur during platoon’s navigation, i.e., leader vehicle failure, obstruction within platoons, multiple platoons come together, etc.
- At the second stage, we implement collision avoidance strategies using V2V and V2I and demonstrate considerable performance increase.
2. Related Work
3. Traffic Flow Modeling for Platoon Navigation
4. Proposed Approach and Implementation Strategy
4.1. Congestion Management Using Platooning
4.1.1. Platoon Formation
- Obtain the O-D matrix and the routes of all vehicles. In the current study, we assume that 60% of vehicles have the same O-D while the remaining 40% share the same route with different destinations.
- The vehicles sharing the same O-D or same route are assigned as members of the platoon. In the present study, we assume that all vehicles are fully autonomous, equipped, and adjacent vehicles on different lanes will change to a common lane during the platoon’s formation.
- Retrieve the list of all vehicles in the current platoon.
- Generate and return a unique ID for the platoon and get the lane of the current platoon.
- Set the kinematic status such as position, speed, velocity, acceleration, etc.
- Obtain the length of each vehicle and total length of the platoon by taking the distance between the leader vehicle’s front bumper and the back bumper of the last vehicle in that platoon.
- Set other parameters such as max platoon size (Including the maximum number of vehicles allowed in the current platoon), inter-platoon spacing (current spacing between vehicles), and the intra-platoon spacing (spacing between two platoons). These parameters help to set operational strategies for platoons in dynamic traffic conditions.
- When a new vehicle wants to join the current platoons, then the “Eligible for merge” criterion is satisfied from its route file, the vehicle is added to the current platoon, and an updated size of the platoon is obtained.
- When the number of vehicles in current platoons is increased from threshold limit (set with other parameters, eight vehicles in our case), then the platoon is split into two or more platoons.
- If two platoons have the same O-D or sub-route to specific O-D and the total number of vehicles of both platoons is less than the threshold size of a platoon, then two platoons could be merged as one.
- If a platoon is no longer active in the scenario, then disband the platoon, and, when all vehicles of current platoons reached their destinations, mark the platoon as dead.
4.1.2. Implementation of Different Scenarios with Dynamically Changing Conditions
When Leader Vehicle Fails
Algorithm 1: Algorithm for platooning scenarios |
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When Multiple Platoons Come Together
Obstruction within Platoons
Algorithm 2: Algorithm for collision Avoidance |
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4.2. Collision Avoidance Using V2V and V2I
5. Simulation Setup
5.1. Network Initialization
5.2. Random Trips
- Randomization: Random output files are created.
- Edge Probabilities: Increases the probability that trips will start/end at the fringe of the network.
- Arrival Rate: By default, this generates vehicles with a constant period and arrival rate.
- Generating vehicles with additional parameters: It includes max speed, vehicle ID, and vehicle class (passenger, driver, etc.).
- Generating modes of traffic: Pedestrians, public transport, or vehicles.
5.3. Tools Used for Simulations
5.4. V2V Using Veins and OMNeT++
5.5. Exception Handling
6. Results and Discussions
7. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Mushtaq, A.; Haq, I.U.; Imtiaz, M.U.; Khan, A.; Shafiq, O. Traffic Flow Management of Autonomous Vehicles Using Deep Reinforcement Learning and Smart Rerouting. IEEE Access 2021, 9, 51005–51019. [Google Scholar] [CrossRef]
- Van, T.N.; Geihs, K. Formal Verification of Multi-agent Plans for Vehicle Platooning. In Context-Aware Systems and Applications, and Nature of Computation and Communication; Springer: Berlin/Heidelberg, Germany, 2020; pp. 3–15. [Google Scholar]
- Rios, J.; Lohn, J. A comparison of optimization approaches for nationwide traffic flow management. In Proceedings of the AIAA Guidance, Navigation, and Control Conference, Chicago, IL, USA, 10–13 August 2009; p. 6010. [Google Scholar]
- Favilla, J.; Machion, A.; Gomide, F. Fuzzy traffic control: Adaptive strategies. In Proceedings of the 2nd IEEE International Conference on Fuzzy Systems, San Francisco, CA, USA, 28 March–1 April 1993; pp. 506–511. [Google Scholar]
- Morando, M.M.; Tian, Q.; Truong, L.T.; Vu, H.L. Studying the safety impact of autonomous vehicles using simulation-based surrogate safety measures. J. Adv. Transp. 2018, 2018, 6135183. [Google Scholar] [CrossRef]
- Kesting, A.; Treiber, M.; Helbing, D. Connectivity statistics of store-and-forward intervehicle communication. IEEE Trans. Intell. Transp. Syst. 2010, 11, 172–181. [Google Scholar] [CrossRef] [Green Version]
- Papageorgiou, M.; Diakaki, C.; Dinopoulou, V.; Kotsialos, A.; Wang, Y. Review of road traffic control strategies. Proc. IEEE 2003, 91, 2043–2067. [Google Scholar] [CrossRef] [Green Version]
- Seiler, P.; Song, B.; Hedrick, J.K. Development of a collision avoidance system. SAE Trans. 1998, 107, 1334–1340. [Google Scholar]
- Fernandes, P.; Nunes, U. Platooning with IVC-enabled autonomous vehicles: Strategies to mitigate communication delays, improve safety and traffic flow. IEEE Trans. Intell. Transp. Syst. 2012, 13, 91–106. [Google Scholar] [CrossRef]
- Abdel-Aty, M.A.; Kitamura, R.; Jovanis, P.P. Using stated preference data for studying the effect of advanced traffic information on drivers’ route choice. Transp. Res. Part C Emerg. Technol. 1997, 5, 39–50. [Google Scholar] [CrossRef]
- Fernandes, P.; Nunes, U. Platooning of autonomous vehicles with intervehicle communications in SUMO traffic simulator. In Proceedings of the 13th International IEEE Conference on Intelligent Transportation Systems, Funchal, Portugal, 19–22 September 2010; pp. 1313–1318. [Google Scholar]
- Kocić, J.; Jovičić, N.; Drndarević, V. Sensors and Sensor Fusion in Autonomous Vehicles. In Proceedings of the 2018 26th Telecommunications Forum (TELFOR), Belgrade, Serbia, 20–21 November 2018; pp. 420–425. [Google Scholar] [CrossRef]
- Zhu, L.; Yu, F.R.; Leung, V.; Wang, H.; Briso-Rodríguez, C.; Zhang, Y. Communications and Networking for Connected Vehicles. Wirel. Commun. Mob. Comput. 2018, 5612785. [Google Scholar] [CrossRef] [Green Version]
- Cheek, E.; Alghodhaifi, H.; Adam, C.; Andres, R.; Lakshmanan, S. Dedicated short range communications used as fail-safe in autonomous navigation. In Proceedings of the Unmanned Systems Technology XXII, Online. 27 April–8 May 2020; p. 114250P. [Google Scholar]
- ElBatt, T.; Goel, S.K.; Holland, G.; Krishnan, H.; Parikh, J. Cooperative collision warning using dedicated short range wireless communications. In Proceedings of the 3rd International Workshop on Vehicular ad Hoc Networks, Los Angeles, CA, USA, 29 September 2006; pp. 1–9. [Google Scholar]
- Wang, C.; Gong, S.; Zhou, A.; Li, T.; Peeta, S. Cooperative adaptive cruise control for connected autonomous vehicles by factoring communication-related constraints. Transp. Res. Procedia 2019, 38, 242–262. [Google Scholar] [CrossRef]
- Seo, H.; Lee, K.D.; Yasukawa, S.; Peng, Y.; Sartori, P. LTE evolution for vehicle-to-everything services. IEEE Commun. Mag. 2016, 54, 22–28. [Google Scholar] [CrossRef]
- Berger, T.; Sallez, Y.; Raileanu, S.; Tahon, C.; Trentesaux, D.; Borangiu, T. Personal rapid transit in an open-control framework. Comput. Ind. Eng. 2011, 61, 300–312. [Google Scholar] [CrossRef]
- Alam, A.; Gattami, A.; Johansson, K.H.; Tomlin, C.J. Guaranteeing safety for heavy duty vehicle platooning: Safe set computations and experimental evaluations. Control Eng. Pract. 2014, 24, 33–41. [Google Scholar] [CrossRef]
- Magdici, S.; Althoff, M. Adaptive cruise control with safety guarantees for autonomous vehicles. IFAC-PapersOnLine 2017, 50, 5774–5781. [Google Scholar] [CrossRef]
- Gong, S.; Zhou, A.; Peeta, S. Cooperative adaptive cruise control for a platoon of connected and autonomous vehicles considering dynamic information flow topology. Transp. Res. Rec. 2019, 2673, 185–198. [Google Scholar] [CrossRef]
- Bang, S.; Ahn, S. Platooning strategy for connected and autonomous vehicles: Transition from light traffic. Transp. Res. Rec. 2017, 2623, 73–81. [Google Scholar] [CrossRef]
- Abualhoul, M.Y.; Shagdar, O.; Nashashibi, F. Visible light inter-vehicle communication for platooning of autonomous vehicles. In Proceedings of the 2016 IEEE Intelligent Vehicles Symposium (IV), Gothenburg, Sweden, 19–22 June 2016; pp. 508–513. [Google Scholar]
- Wang, J.; Gong, S.; Peeta, S.; Lu, L. A real-time deployable model predictive control-based cooperative platooning approach for connected and autonomous vehicles. Transp. Res. Part B Methodol. 2019, 128, 271–301. [Google Scholar] [CrossRef]
- Rezgui, J.; Gagné, É.; Blain, G.; St-Pierre, O.; Harvey, M. Platooning of Autonomous Vehicles with Artificial Intelligence V2I Communications and Navigation Algorithm. In Proceedings of the 2020 Global Information Infrastructure and Networking Symposium (GIIS), Tunis, Tunisia, 28–30 October 2020; pp. 1–6. [Google Scholar]
- Firoozi, R.; Zhang, X.; Borrelli, F. Formation and reconfiguration of tight multi-lane platoons. Control Eng. Pract. 2021, 108, 104714. [Google Scholar] [CrossRef]
- Horowitz, R.; Tan, C.W.; Sun, X. An Efficient Lane Change Maneuver for Platoons of Vehicles in an Automated Highway System; eScholarship: Los Angeles, CA, USA, 2004. [Google Scholar]
- Wang, Z.; Wu, G.; Hao, P.; Boriboonsomsin, K.; Barth, M. Developing a platoon-wide eco-cooperative adaptive cruise control (CACC) system. In Proceedings of the 2017 IEEE Intelligent Vehicles Symposium (IV), Los Angeles, CA, USA, 11–14 June 2017; pp. 1256–1261. [Google Scholar]
- Canudas de Wit, C.; Brogliato, B. Stability issues for vehicle platooning in automated highway systems. In Proceedings of the 1999 IEEE International Conference on Control Applications (Cat. No.99CH36328), Kohala Coast, HI, USA, 22–27 August 1999; Volume 2, pp. 1377–1382. [Google Scholar] [CrossRef]
- Tatchikou, R.; Biswas, S.; Dion, F. Cooperative vehicle collision avoidance using inter-vehicle packet forwarding. In Proceedings of the GLOBECOM’05, IEEE Global Telecommunications Conference, St. Louis, MO, USA, 28 November–2 December 2005; Volume 5, p. 5. [Google Scholar]
- Garcia-Roger, D.; González, E.E.; Martín-Sacristán, D.; Monserrat, J.F. V2X support in 3GPP specifications: From 4G to 5G and beyond. IEEE Access 2020, 8, 190946–190963. [Google Scholar] [CrossRef]
- Etsi, G. Service Requirements for Enhanced V2X Scenarios; 3GPP TS 22.186; ETSI: Sophia Antipolis, France, 2019. [Google Scholar]
- Vukadinovic, V.; Bakowski, K.; Marsch, P.; Garcia, I.D.; Xu, H.; Sybis, M.; Sroka, P.; Wesolowski, K.; Lister, D.; Thibault, I. 3GPP C-V2X and IEEE 802.11 p for Vehicle-to-Vehicle communications in highway platooning scenarios. Ad Hoc Netw. 2018, 74, 17–29. [Google Scholar] [CrossRef]
- Nardini, G.; Virdis, A.; Campolo, C.; Molinaro, A.; Stea, G. Cellular-V2X communications for platooning: Design and evaluation. Sensors 2018, 18, 1527. [Google Scholar] [CrossRef] [Green Version]
- Choi, Y.; Jimenez, H.; Mavris, D.N. Two-layer obstacle collision avoidance with machine learning for more energy-efficient unmanned aircraft trajectories. Robot. Auton. Syst. 2017, 98, 158–173. [Google Scholar] [CrossRef]
- Tung, L.C.; Mena, J.; Gerla, M.; Sommer, C. A cluster based architecture for intersection collision avoidance using heterogeneous networks. In Proceedings of the 2013 12th Annual Mediterranean Ad Hoc Networking Workshop (MED-HOC-NET), Ajaccio, France, 24–26 June 2013; pp. 82–88. [Google Scholar]
- Yang, J.; Coughlin, J.F. In-vehicle technology for self-driving cars: Advantages and challenges for aging drivers. Int. J. Automot. Technol. 2014, 15, 333–340. [Google Scholar] [CrossRef]
- Wikipedia. Traffic Flow. 2021. Available online: https://en.wikipedia.org/wiki/Traffic_flow (accessed on 5 March 2021).
- Treiber, M.; Kesting, A. Traffic flow dynamics. In Traffic Flow Dynamics: Data, Models and Simulation; Springer: Berlin/Heidelberg, Germany, 2013. [Google Scholar]
- Edie, L.C. Discussion of Traffic Stream Measurements and Definitions; Port of New York Authority: New York, NY, USA, 1963.
- Michaud, F.; Lepage, P.; Frenette, P.; Letourneau, D.; Gaubert, N. Coordinated maneuvering of automated vehicles in platoons. IEEE Trans. Intell. Transp. Syst. 2006, 7, 437–447. [Google Scholar] [CrossRef]
- Kesting, A.; Treiber, M.; Schönhof, M.; Helbing, D. Adaptive cruise control design for active congestion avoidance. Transp. Res. Part C Emerg. Technol. 2008, 16, 668–683. [Google Scholar] [CrossRef]
- Lopez, P.A.; Behrisch, M.; Bieker-Walz, L.; Erdmann, J.; Flötteröd, Y.P.; Hilbrich, R.; Lücken, L.; Rummel, J.; Wagner, P.; Wießner, E. Microscopic Traffic Simulation using SUMO. In Proceedings of the The 21st IEEE International Conference on Intelligent Transportation Systems, Maui, HI, USA, 4–7 November 2018. [Google Scholar]
- Sommer, C.; Eckhoff, D.; Brummer, A.; Buse, D.S.; Hagenauer, F.; Joerer, S.; Segata, M. Veins: The open source vehicular network simulation framework. In Recent Advances in Network Simulation; Springer: Berlin/Heidelberg, Germany, 2019; pp. 215–252. [Google Scholar]
- Varga, A.; Hornig, R. An overview of the OMNeT++ simulation environment. In Proceedings of the 1st International Conference on Simulation Tools and Techniques for Communications, Networks and Systems & Workshops, Marseille, France, 3–7 March 2008; pp. 1–10. [Google Scholar]
- Sliwa, B.; Pillmann, J.; Eckermann, F.; Habel, L.; Schreckenberg, M.; Wietfeld, C. Lightweight joint simulation of vehicular mobility and communication with LIMoSim. In Proceedings of the 2017 IEEE Vehicular Networking Conference (VNC), Turin, Italy, 27–29 November 2017; pp. 81–88. [Google Scholar]
Simulation Parameters | |
---|---|
Parameter | Value |
Simulation Tools | SUMO, Veins, OMNet++ |
Number of Vehicles used | 600, 4000 |
Intersection used | 01 |
Number of traffic lights used | 04 |
Traffic light green duration | 30 (s) |
Simulation Map | 4 leg intersection |
Lanes | 4 lanes on each leg |
Desired distance | 0.4 (m) |
Tau | 1 (s) |
Imperfection | 0.5 (s) |
Critical density () | 40 vehicles |
Jam density () | 55 vehicles |
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Mushtaq, A.; Haq, I.u.; Nabi, W.u.; Khan, A.; Shafiq, O. Traffic Flow Management of Autonomous Vehicles Using Platooning and Collision Avoidance Strategies. Electronics 2021, 10, 1221. https://doi.org/10.3390/electronics10101221
Mushtaq A, Haq Iu, Nabi Wu, Khan A, Shafiq O. Traffic Flow Management of Autonomous Vehicles Using Platooning and Collision Avoidance Strategies. Electronics. 2021; 10(10):1221. https://doi.org/10.3390/electronics10101221
Chicago/Turabian StyleMushtaq, Anum, Irfan ul Haq, Wajih un Nabi, Asifullah Khan, and Omair Shafiq. 2021. "Traffic Flow Management of Autonomous Vehicles Using Platooning and Collision Avoidance Strategies" Electronics 10, no. 10: 1221. https://doi.org/10.3390/electronics10101221