Path planning and re-planning of lane change manoeuvres in dynamic traffic environments
17 May 2019-International Journal of Vehicle Autonomous Systems (Inderscience Publishers)-Vol. 14, Iss: 3, pp 239
TL;DR: In this study, a novel path for lane change manoeuvres, based on mathematical functions, is introduced and the analytical results showed that the designed paths are suitable, comfortable, and safe.
Abstract: Automatic lane change is of utmost importance in designing autonomous vehicles and driver assistant systems. In this study, a novel path for lane change manoeuvres, based on mathematical functions, is introduced. To obtain a suitable path for lane change manoeuvres, four functions, namely quintic, septic, sinusoidal, and tangent functions, were examined. The analysis revealed that, according to the ISO Standards and peak acceleration criterion, a quintic function has the advantage of passenger comfort over other path functions. After choosing the appropriate path, an algorithm for re-planning the lane change path, based on dynamic traffic conditions, was proposed. The simulation results show that the proposed algorithm is capable of designing the path in various traffic conditions. Moreover, the algorithm can navigate the vehicle to the initial lane, if the manoeuvre is not possible. Our analytical results showed that the designed paths are suitable, comfortable, and safe.
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TL;DR: This paper investigates a new approach to deal with the path-tracking for an uncertain autonomous ground vehicle (AGV) exposed to the unknown external disturbances caused by abrupt changes in the road bank angle with a robust adaptive non-singular fast terminal sliding mode control (RANFTSMC).
Abstract: This paper investigates a new approach to deal with the path-tracking for an uncertain autonomous ground vehicle (AGV) exposed to the unknown external disturbances caused by abrupt changes in the road bank angle. A robust adaptive non-singular fast terminal sliding mode control (RANFTSMC) is designed for the lane change maneuver (LCM) lateral control. Initially, a reference LCM path is planned by a recent procedure. Then, assuming that the upper bound of unknown external disturbances and uncertainties is known, a robust steering controller is implemented via non-singular fast terminal sliding mode control (NFTSMC) to ensure the vehicle lateral stability. However, the upper limit of the vehicle uncertainties and perturbations is unknown in real-world driving situations, therefore, an adaptive tuning law is developed to estimate this unknown upper bound. As long as the efficiency of the controller depends on their parameters, a meta-heuristic optimization algorithm is employed to deliver the controller optimal settings. The effectiveness of the proposed RANFTSMC is proven with theoretical illustrations and numerical simulation results. The offered steering controller maintains the lateral stability even in severe conditions where the vehicle longitudinal speed is up to 120 km/h. In this connection, the suggested strategy is compared with the higher-order sliding mode controller (HOSMC) and conventional sliding mode controller (SMC).
10 citations
TL;DR: In this paper , an optimization method based on the combination of the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), which provides a new idea for solving the multi-objective optimization problem of lane change trajectory algorithm, is proposed.
Abstract: Among so many autonomous driving technologies, autonomous lane changing is an important application scenario, which has been gaining increasing amounts of attention from both industry and academic communities because it can effectively reduce traffic congestion and improve road safety. However, most of the existing researchers transform the multi-objective optimization problem of lane changing trajectory into a single objective problem, but how to determine the weight of the objective function is relatively fuzzy. Therefore, an optimization method based on the combination of the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), which provides a new idea for solving the multi-objective optimization problem of lane change trajectory algorithm, is proposed in this paper. Firstly, considering the constraints of lane changing and combining with the collision detection algorithm, the feasible lane changing trajectory cluster is obtained based on the quintic polynomial. In order to ensure the comfort, stability and high efficiency of the lane changing process, the NSGA-II Algorithm is used to optimize the longitudinal displacement and time of lane changing. The continuous ordered weighted averaging (COWA) operator is introduced to calculate the weights of three objective optimization functions. Finally, the TOPSIS Algorithm is applied to obtain the optimal lane change trajectory. The simulations are conducted, and the results demonstrate that the proposed method can generate a satisfactory trajectory for automatic lane changing actions.
1 citations
TL;DR: In this paper , the integration of Machine Learning (ML) and Model Predictive Control (MPC) in Automotive Control System (ACS) applications is discussed and the main focus of this paper is how MPC in ML-based ACS applications ensures stability while meeting constraint is also discussed.
TL;DR: In this paper , a real-time capable implementation of an algorithm for an automated lane change with the capability of dynamic re-planning in case the environmental situation changes, e.g. other traffic participants change their intention and the previously calculated trajectory does not guarantee safety anymore.
Abstract: Lane changes are frequent maneuvers in everyday driving and have to be included in automated driving functions. We present a real-time capable implementation of an algorithm for an automated lane change with the capability of dynamic re-planning in case the environmental situation changes, e.g. other traffic participants change their intention and the previously calculated trajectory does not guarantee safety anymore. Trajectories are described by polynomials and stored in look-up tables for different longitudinal velocities, longitudinal accelerations and maneuver duration times. Using a single-track model within a linear-quadratic control, the lateral deviation to the reference trajectory and the vehicle’s orientation are controlled. The influence of parameter changes from additional payload, tire-road friction coefficients and tire properties on the vehicle-controller interaction are investigated with a sensitivity analysis on data generated with a simulation. The performance of the algorithm is presented and it is shown that many parameter variations will probably be perceived by occupants, but will not lead to safety-critical situations.