Optimal Vehicle Trajectory Planning With Control Constraints and Recursive Implementation for Automated On-Ramp Merging

Abstract

This paper proposes a vehicle trajectory planning method for automated on-ramp merging. Trajectory planning tasks of an on-ramp merging vehicle and a mainline facilitating vehicle are formulated as two related optimal control problems. Rather than specifying the merge point via external computational procedures, the location and time that the onramp vehicle merges into the mainline are determined endogenously by the optimal control problem of the facilitating vehicle. Bounds on vehicle acceleration are explicitly considered. The Pontryagin Maximum Principle is applied to find the solutions of the optimal control problems. In order to accommodate the constantly changing external environment, the proposed optimal control method is subsequently implemented in a recursive planning framework. Because of the nature of the problem, the length of the planning horizon is timevarying, unlike conventional model predictive control applications where the planning horizon is of fixed length. Numerical experiments are conducted to study performances of the proposed methodology under the influence of different leading vehicle trajectories and with different lengths of the planning updating interval. In particular, an experiment involving a real-world leading vehicle trajectory and considering different traffic demand levels are presented. The proposed methodology performs well in these experiments and has demonstrated a good potential in real-time applications.

Energy Efficient and Safe Control Strategy for Electric Vehicles Including Driver Preference

“…Equation ( 7) forms a nonlinear two-point boundary value problem (TPBVP) whose solution usually requires numerical techniques (e.g., multiple shooting methods) [12,36]. The long-term optimisation is customarily split into acceleration and deceleration modes with a free final time.…”

Dynamic motion planner with trajectory optimisation for automated highway lane‐changing driving

“…In this research, the desired safe distance G i (t) (i ∈ (PT, FT, PC, FC)) is defined as a maximum among safe following distance G safe and a minimum allowed distance G min , which can be expressed as follows [26,27]: It should be noted that the target safety interval can be calculated by formula (9), but how to use the current information to estimate the terminal positions x FT t f and x PT t f is still a key technical challenge. In this paper, the future velocity of the vehicles PT and FT is assumed to be the same as the instant when the sampling period updates, as in line with previous researches [28,29]. Thus, the final longitudinal position can be calculated by the following equation:…”

“…It should be noted that the target safety interval can be calculated by formula (9), but how to use the current information to estimate the terminal positions and is still a key technical challenge. In this paper, the future velocity of the vehicles PT and FT is assumed to be the same as the instant when the sampling period updates, as in line with previous researches [28, 29]. Thus, the final longitudinal position can be calculated by the following equation: where is the vehicle's longitudinal position of current sampling time, and means the current longitudinal velocity.…”

The State-of-the-Art of Coordinated Ramp Control with Mixed Traffic Conditions

“…Dao et al proposed a distributed control protocol to assign vehicles into vehicle strings in the merging scenario [76]. Zhou et al developed a vehicle trajectory planning method for CAV coordination at ramp, formulating the planning tasks of the ramp vehicle and the mainline vehicle as two related distributed optimal problems [77]. Wang et al proposed a distributed consensus-based CAV coordination system [78].…”