This study presents an optimization-based path-planning algorithm for connected and non-connected automated vehicles on multilane motorways. The algorithm is derived from an optimal control problem formulation, with the objective function incorporating terms related to vehicle advancement, passenger comfort, collision avoidance, road departures, and negative speeds. Connected vehicles are able to exchange real-time information about their last generated path with each other or the infrastructure. The numerical solution of the optimal control problem utilizes an efficient feasible direction algorithm, while a simplified Dynamic Programming algorithm provides the initial guess trajectory. The approach is designed to be executed within a model predictive control (MPC) framework and is embedded in the Aimsun microsimulation platform for realistic evaluation. The study uses a homogeneous motorway section as a testbed, with three lanes and two different levels of inflow simulated. Vehicle trajectories and traffic conditions are monitored over a 60-minute simulation horizon. Both automated and manually driven vehicles are randomly assigned characteristics such as vehicle type, dimensions, desired speed, initial lane, and time gap. For automated vehicles (AVs), a planning horizon of 8 seconds with a path-planning step of 0.25 seconds is used. Results show that optimally controlled AVs drive closer to their desired speed compared to ordinary Aimsun vehicles. Increased penetration rates of AVs improve the efficiency of overall traffic flow, benefiting manual vehicles as well. Additionally, connected controlled vehicles demonstrate higher efficiency compared to non-connected controlled vehicles due to improved real-time information and short-term prediction. In summary, this study presents an optimization-based path-planning algorithm which improves vehicle advancement goals while considering passenger comfort and collision avoidance objectives for connected and non-connected automated vehicles on multilane motorways. Results indicate increased efficiency in driving closer to desired speeds for optimally controlled AVs compared to ordinary vehicles; furthermore increased penetration rates of AVs enhance overall traffic flow efficiency with connected controlled vehicles demonstrating higher efficiency than non-connected ones.
- Researchers have created a special computer program to help self-driving cars plan their routes on highways.
- The program considers things like how fast the car can go, how comfortable the ride is for passengers, and how to avoid crashing or going off the road.
- Self-driving cars can talk to each other and to the road infrastructure in real-time to share important information.
- The program uses math to find the best route for the cars, and it can do this quickly and efficiently.
- When more self-driving cars are on the road, they can make traffic flow better for all cars, including ones driven by people.
Definitions
- Optimization-based: Using math and logic to find the best solution
- Path-planning algorithm: A set of instructions that helps a car decide which way to go
- Connected vehicles: Cars that can communicate with each other or with the road infrastructure
- Numerical solution: Finding an answer using numbers and calculations
- Model predictive control (MPC) framework: A system that helps control how a car moves based on predictions of what will happen next
Optimization-Based Path-Planning Algorithm for Connected and Non-Connected Automated Vehicles on Multilane Motorways
Automated vehicles (AVs) are becoming increasingly popular due to their potential to improve safety, reduce traffic congestion, and provide passengers with a more comfortable ride. To realize these benefits, an efficient path planning algorithm is needed that can generate optimal trajectories for AVs while considering passenger comfort, collision avoidance objectives, road departures, negative speeds and other factors. This study presents an optimization-based path-planning algorithm derived from an optimal control problem formulation that meets these requirements.
Problem Formulation
The proposed algorithm is based on an optimal control problem formulation which incorporates terms related to vehicle advancement goals as well as passenger comfort and collision avoidance objectives. Connected vehicles are able to exchange real-time information about their last generated path with each other or the infrastructure. The numerical solution of the optimal control problem utilizes an efficient feasible direction algorithm while a simplified Dynamic Programming algorithm provides the initial guess trajectory. The approach is designed to be executed within a model predictive control (MPC) framework and is embedded in the Aimsun microsimulation platform for realistic evaluation.
Testbed Setup
To evaluate the effectiveness of the proposed approach, a homogeneous motorway section was used as a testbed with three lanes and two different levels of inflow simulated over a 60 minute simulation horizon. Vehicle trajectories were monitored throughout this period with both automated and manually driven vehicles randomly assigned characteristics such as vehicle type, dimensions, desired speed, initial lane position and time gap between them at any given point in time. For automated vehicles (AVs), a planning horizon of 8 seconds with a path-planning step of 0.25 seconds was used during simulations..
Results
Results showed that optimally controlled AVs drove closer to their desired speed compared to ordinary Aimsun vehicles; furthermore increased penetration rates of AVs improved overall traffic flow efficiency benefiting manual drivers too! Additionally connected controlled vehicles demonstrated higher efficiency than non-connected ones due to improved real-time information sharing capabilities combined with short term prediction algorithms employed by them during simulations .
Conclusion
In conclusion this study presents an optimization based path planning algorithm which improves vehicle advancement goals while considering passenger comfort and collision avoidance objectives for connected and non connected automated vehicles on multilane motorways . Results indicate increased efficiency in driving closer to desired speeds for optimally controlled AVs compared to ordinary ones; furthermore increased penetration rates of AVs enhance overall traffic flow efficiency significantly ,with connected controlled vehicles demonstrating even higher efficiencies than non connected ones .
A path-planning algorithm for connected and non-connected automated road vehicles on multilane motorways is derived from the opportune formulation of an optimal control problem. In this framework, the objective function to be minimized contains appropriate respective terms to reflect: the goals of vehicle advancement; passenger comfort; and avoidance of collisions with other vehicles, of road departures and of negative speeds. Connectivity implies that connected vehicles are able to exchange with each other (V2V) or the infrastructure (V2I), real-time information about their last generated path. For the numerical solution of the optimal control problem, an efficient feasible direction algorithm is used. To ensure high-quality local minima, a simplified Dynamic Programming algorithm is also conceived to deliver the initial guess trajectory for the feasible direction algorithm. Thanks to low computation times, the approach is readily executable within a model predictive control (MPC) framework. The proposed MPC-based approach is embedded within the Aimsun microsimulation platform, which enables the evaluation of a plethora of realistic vehicle driving and advancement scenarios. Results obtained on a multilane motorway stretch indicate higher efficiency of the optimally controlled vehicles in driving closer to their desired speed, compared to ordinary Aimsun vehicles. Increased penetration rates of automated vehicles are found to increase the efficiency of the overall traffic flow, benefiting manual vehicles as well. Moreover, connected controlled vehicles appear to be more efficient compared to the corresponding non-connected controlled vehicles, due to the improved real-time information and short-term prediction.
