Integrated trajectory planning and control for obstacle avoidance manoeuvre using nonlinear vehicle model-predictive algorithm

In the current literature, model-predictive (MP) algorithm is widely applied in autonomous vehicle trajectory planning and control but most of the current studies only apply the linear tyre model, which cannot accurately present the tyre non-linear characteristic. Furthermore, most of these studies separately consider the trajectory planning and trajectory control of the autonomous vehicle and few of them have integrated the trajectory planning and trajectory control together. To fill in above research gaps, this study proposes the integrated trajectory planning and trajectory control method using a non-linear vehicle MP algorithm. To fully utilise the advantages of four-wheel-independent-steering and four-wheel-independent-driving vehicle, the MP algorithm is proposed based on four-wheel dynamics model and non-linear Dugoff tyre model. This study also proposes the mathematical modelling of the static obstacle and dynamic obstacle for the obstacle avoidance manoeuvre of the autonomous vehicle. Finally, simulation results have been presented to show the effectiveness of the proposed control method

Model Predictive Control as a Function for Trajectory Control during High Dynamic Vehicle Maneuvers considering Actuator Constraints

Autonomous driving is a rapidly growing field and can bring significant transition in mobility and transportation. In order to cater a safe and reliable autonomous driving operation, all the systems concerning with perception, planning and control has to be highly efficient. MPC is a control technique used to control vehicle motion by controlling actuators based on vehicle model and its constraints. The uniqueness of MPC compared to other controllers is its ability to predict future states of the vehicle using the derived vehicle model. Due to the technological development & increase in computational capacity of processors and optimization algorithms MPC is adopted for real-time application in dynamic environments. This research focuses on using Model predictive Control (MPC) to control the trajectory of an autonomous vehicle controlling the vehicle actuators for high dynamic maneuvers. Vehicle Models considering kinematics and vehicle dynamics is developed. These models are used for MPC as prediction models and the performance of MPC is evaluated. MPC trajectory control is performed with the minimization of cost function and limiting constraints. MATLAB/Simulink is used for designing trajectory control system and interfaced with CarMaker for evaluating controller performance in a realistic simulation environment. Performance of MPC with kinematic and dynamic vehicle models for high dynamic maneuvers is evaluated with different speed profiles

DEVELOPMENT OF AUTONOMOUS VEHICLE MOTION PLANNING AND CONTROL ALGORITHM WITH D* PLANNER AND MODEL PREDICTIVE CONTROL IN A DYNAMIC ENVIRONMENT

The research in this report incorporates the improvement in the autonomous driving capability of self-driving cars in a dynamic environment. Global and local path planning are implemented using the D* path planning algorithm with a combined Cubic B-Spline trajectory generator, which generates an optimal obstacle free trajectory for the vehicle to follow and avoid collision. Model Predictive Control (MPC) is used for the longitudinal and the lateral control of the vehicle. The presented motion planning and control algorithm is tested using Model-In-the-Loop (MIL) method with the help of MATLAB® Driving Scenario Designer and Unreal Engine® Simulator by Epic Games®. Different traffic scenarios are built, and a camera sensor is configured to simulate the sensory data and feed it to the controller for further processing and vehicle motion planning. Simulation results of vehicle motion control with global and local path planning for dynamic obstacle avoidance are presented. The simulation results show that an autonomous vehicle follows a commanded velocity when the relative distance between the ego vehicle and an obstacle is greater than a calculated safe distance. When the relative distance is close to the safe distance, the ego vehicle maintains the headway. When an obstacle is detected by the ego vehicle and the ego vehicle wants to pass the obstacle, the ego vehicle performs obstacle avoidance maneuver by tracking desired lateral positions

Dynamic Lane-Changing Trajectory Planning for Autonomous Vehicles Based on Discrete Global Trajectory

Automatic lane-changing is a complex and critical task for autonomous vehicle control. Existing researches on autonomous vehicle technology mainly focus on avoiding obstacles; however, few studies have accounted for dynamic lane changing based on some certain assumptions, such as the lane-changing speed is constant or the terminal state is known in advance. In this study, a typical lane-changing scenario is developed with the consideration of preceding and lagging vehicles on the road. Based on the local trajectory generated by the global positioning system, a path planning model and a speed planning model are respectively established through the cubic polynomial interpolation. To guarantee the driving safety, passenger comfort and vehicle efficiency, a comprehensive trajectory optimization function is proposed according to the path planning model and speed planning model. In addition, a dynamic decoupling model is established to solve the problems of real-time application to provide viable solutions. The simulations and real vehicle validations are conducted, and the results highlight that the proposed method can generate a satisfactory lane-changing trajectory for automatic lane-changing actions

Team MIT Urban Challenge Technical Report

This technical report describes Team MITs approach to theDARPA Urban Challenge. We have developed a novel strategy forusing many inexpensive sensors, mounted on the vehicle periphery,and calibrated with a new cross-­modal calibrationtechnique. Lidar, camera, and radar data streams are processedusing an innovative, locally smooth state representation thatprovides robust perception for real­ time autonomous control. Aresilient planning and control architecture has been developedfor driving in traffic, comprised of an innovative combination ofwell­proven algorithms for mission planning, situationalplanning, situational interpretation, and trajectory control. These innovations are being incorporated in two new roboticvehicles equipped for autonomous driving in urban environments,with extensive testing on a DARPA site visit course. Experimentalresults demonstrate all basic navigation and some basic trafficbehaviors, including unoccupied autonomous driving, lanefollowing using pure-­pursuit control and our local frameperception strategy, obstacle avoidance using kino-­dynamic RRTpath planning, U-­turns, and precedence evaluation amongst othercars at intersections using our situational interpreter. We areworking to extend these approaches to advanced navigation andtraffic scenarios

Interaction-Aware Motion Planning for Autonomous Vehicles with Multi-Modal Obstacle Uncertainty Predictions

This paper proposes an interaction and safety-aware motion-planning method for an autonomous vehicle in uncertain multi-vehicle traffic environments. The method integrates the ability of the interaction-aware interacting multiple model Kalman filter (IAIMM-KF) to predict interactive multi-modal maneuvers of surrounding vehicles, and the advantage of model predictive control (MPC) in planning an optimal trajectory in uncertain dynamic environments. The multi-modal prediction uncertainties, containing both the maneuver and trajectory uncertainties of surrounding vehicles, are considered in computing the reference targets and designing the collision-avoidance constraints of MPC for resilient motion planning of the ego vehicle. The MPC achieves safety awareness by incorporating a tunable parameter to adjust the predicted obstacle occupancy in the design of the safety constraints, allowing the approach to achieve a trade-off between performance and robustness. Based on the prediction of the surrounding vehicles, an optimal reference trajectory of the ego vehicle is computed by MPC to follow the time-varying reference targets and avoid collisions with obstacles. The efficiency of the method is illustrated in challenging highway-driving simulation scenarios and a driving scenario from a recorded traffic dataset.Comment: 15 page