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 |
When Multiple Platoons Come Together
Obstruction within Platoons
Algorithm 2: Algorithm for collision Avoidance |
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
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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