521 research outputs found
Safety-Aware Longitudinal and Lateral Control of Autonomous Vehicles
Safety is undoubtedly the most critical design requirement regarding autonomous vehicle controllers. This research considers an autonomous vehicle to keep a desired distance from the leader vehicle, as well as stay centered within the lane. To achieve this, the lateral control problem and the combined longitudinal and lateral control problem were studied. Adaptive control laws were proposed with the aid of the backstepping technique and the barrier function technique. Simulation was done to verify the effectiveness of the proposed control laws
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
Integrating Vehicle Slip and Yaw in Overarching Multi-Tiered Automated Vehicle Steering Control to Balance Path Following Accuracy, Gracefulness, and Safety
Balancing path following accuracy and error convergence with graceful motion
in steering control is challenging due to the competing nature of these
requirements, especially across a range of operating speeds and conditions.
This paper demonstrates that an integrated multi-tiered steering controller
considering the impact of slip on kinematic control, dynamic control, and
steering actuator rate commands achieves accurate and graceful path following.
This work is founded on multi-tiered sideslip and yaw-based models, which allow
derivation of controllers considering error due to sideslip and the mapping
between steering commands and graceful lateral motion. Observer based sideslip
estimates are combined with heading error in the kinematic controller to
provide feedforward slip compensation. Path following error is compensated by a
continuous Variable Structure Controller (VSC) using speed-based path manifolds
to balance graceful motion and error convergence. Resulting yaw rate commands
are used by a backstepping dynamic controller to generate steering rate
commands. A High Gain Observer (HGO) estimates sideslip and yaw rate for output
feedback control. Stability analysis of the output feedback controller is
provided, and peaking is resolved. The work focuses on lateral control alone so
that the steering controller can be combined with other speed controllers.
Field results provide comparisons to related approaches demonstrating
gracefulness and accuracy in different complex scenarios with varied weather
conditions and perturbations
A Systematic Survey of Control Techniques and Applications: From Autonomous Vehicles to Connected and Automated Vehicles
Vehicle control is one of the most critical challenges in autonomous vehicles
(AVs) and connected and automated vehicles (CAVs), and it is paramount in
vehicle safety, passenger comfort, transportation efficiency, and energy
saving. This survey attempts to provide a comprehensive and thorough overview
of the current state of vehicle control technology, focusing on the evolution
from vehicle state estimation and trajectory tracking control in AVs at the
microscopic level to collaborative control in CAVs at the macroscopic level.
First, this review starts with vehicle key state estimation, specifically
vehicle sideslip angle, which is the most pivotal state for vehicle trajectory
control, to discuss representative approaches. Then, we present symbolic
vehicle trajectory tracking control approaches for AVs. On top of that, we
further review the collaborative control frameworks for CAVs and corresponding
applications. Finally, this survey concludes with a discussion of future
research directions and the challenges. This survey aims to provide a
contextualized and in-depth look at state of the art in vehicle control for AVs
and CAVs, identifying critical areas of focus and pointing out the potential
areas for further exploration
Real time implementation of socially acceptable collision avoidance of a low speed autonomous shuttle using the elastic band method
This paper presents the real time implementation of socially acceptable collision avoidance using the elastic band method for low speed autonomous shuttles operating in high pedestrian density environments. The modeling and validation of the research autonomous vehicle used in the experimental implementation is presented first, followed by the details of the Hardware-In-the-Loop connected and autonomous vehicle simulator used. The socially acceptable collision avoidance algorithm is formulated using the elastic band method as an online, local path modification algorithm. Parameter space based robust feedback plus feedforward steering controller design is used. Model-in-the-loop, Hardware-In-the-Loop and road testing in a proving ground are used to demonstrate the effectiveness of the real time implementation of the elastic band based socially acceptable collision avoidance method of this paper
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