Sensor Fusion and Non-linear MPC controller development studies for Intelligent Autonomous vehicular systems

The demand for safety and fuel efficiency on ground vehicles and advancement in embedded systems created the opportunity to develop Autonomous controller. The present thesis work is three fold and it encompasses all elements that are required to prototype the autonomous intelligent system including simulation, state handling and real time implementation. The Autonomous vehicle operation is mainly dependent upon accurate state estimation and thus a major concern of implementing the autonomous navigation is obtaining robust and accurate data from sensors. This is especially true, in case of Inertial Measurement Unit (IMU) sensor data. The IMU consists of a 3-axis gyro, 3-axis accelerometer, and 3-axis magnetometer. The IMU provides vehicle orientation in 3D space in terms of yaw, roll and pitch. Out of which, yaw is a major parameter to control the ground vehicle’s lateral position during navigation. The accelerometer is responsible for attitude (roll-pitch) estimates and magnetometer is responsible for yaw estimates. However, the magnetometer is prone to environmental magnetic disturbances which induce errors in the measurement. The initial work focuses on alleviating magnetic disturbances for ground vehicles by fusing the vehicle kinematics information with IMU senor in an Extended Kalman filter (EKF) with the vehicle orientation represented using Quaternions. The previous studies covers the handling of sensor noise data for vehicle yaw estimations and the same approach can be applied for additional sensors used in the work. However, it is important to develop simulations to analyze the autonomous navigation for various road, obstacles and grade conditions. These simulations serve base platform for real time implementation and provide the opportunity to implement it on real road vehicular application and leads to prototype the controller. Therefore, the next section deals with simulations that focuses on developing Non-linear Model Predictive controller for high speed off-road autonomous vehicle, which avoids undesirable conditions including stationary obstacles, moving obstacles and steep regions while maintaining the vehicle safety from rollover. The NMPC controller is developed using CasADi tools in MATLAB environment. As mentioned, the above two sections provide base platform for real time implementation and the final section uses these techniques for developing intelligent autonomous vehicular system that would track the given path and avoid static obstacles by rejecting the considerable environmental disturbance in the given path. The Linear Quadratic Gaussian (LQG) is developed for the present application, The model developed in the LQG controller is a kinematic bicycle model, that mimics 1/5th scale truck and cubic spline has been used to connect and generate the continuous target path

Studies on Simulation and Real Time Implementation of LQG Controller for Autonomous Navigation

The advancement in embedded systems and positional accuracy with base station GPS modules created opportunity to develop high performance autonomous ground vehicles. However, the development of vehicle model and making accurate state estimations play vital role in reducing the cross track error. The present research focus on developing Linear Quadratic Gaussian (LQG) with Kalman estimator for autonomous ground vehicle to track various routes, that are made with the series of waypoints. The model developed in the LQG controller is a kinematic bicycle model, which mimics 1/5th scale truck. Further, the cubic spline fit has been used to connect the waypoints and generate the continuous desired/target path. The testing and implementation has been done at APS labs, MTU on the mentioned vehicle to study the performance of controller. Python has been used for simulations, controller coding and interfacing the sensors with controller. From the results, it has been confirmed that, the vehicle is able to track the given path within the cross track error of ±0.2m

Alleviating the Magnetic Effects on Magnetometers Using Vehicle Kinematics for Yaw Estimation for Autonomous Ground Vehicles

Autonomous vehicle operation is dependent upon accurate position estimation and thus a major concern of implementing the autonomous navigation is obtaining robust and accurate data from sensors. This is especially true, in case of Inertial Measurement Unit (IMU) sensor data. The IMU consists of a 3-axis gyro, 3-axis accelerometer, and 3-axis magnetometer. The IMU provides vehicle orientation in 3D space in terms of yaw, roll and pitch. Out of which, yaw is a major parameter to control the ground vehicle\u27s lateral position during navigation. The accelerometer is responsible for attitude (roll-pitch) estimates and magnetometer is responsible for yaw estimates. However, the magnetometer is prone to environmental magnetic disturbances which induce errors in the measurement. The present work focuses on alleviating magnetic disturbances for ground vehicles by fusing the vehicle kinematics information with IMU senor in an Extended Kalman filter (EKF) with the vehicle orientation represented using Quaternions. In addition, the error in rate measurements from gyro sensor gets accumulated as the time progress which results in drift in rate measurements and thus affecting the vehicle orientation estimation. To resolve and account for the gyro drift, the EKF algorithm includes gyro bias terms in state vector, which augments the state vector with 4 Quaternions and 3 gyro bias vectors. The proposed modified EKF strategy has been experimentally tested and validated on 1/5th scale buggy type truck. The developed EKF, analysis and results are present which shows that, while the vehicle is affected by up to 1 ± 0.8 Norm of magnetic field and based on the curvature of the road it can reduce the RMS errors in yaw estimations from 3.4 to 0.5° in straight path and from 6.0 to 1.9° during tuning paths. Due to high accuracy in speed sensor and steering angle measurements, this fusion algorithm is robust and can make yaw estimations within ±1.5° heading error for about 30-meter distance

Algorithm Development for Avoiding Both Moving and Stationary Obstacles in an Unstructured High-Speed Autonomous Vehicular Application Using a Nonlinear Model Predictive Controller

The advancement in vision sensors and embedded technology created the opportunity in autonomous vehicles to look ahead in the future to avoid potential obstacles and steep regions to reach the target location as soon as possible and yet maintain vehicle safety from rollover. The present work focuses on developing a nonlinear model predictive controller (NMPC) for a high-speed off-road autonomous vehicle, which avoids undesirable conditions including stationary obstacles, moving obstacles, and steep regions while maintaining the vehicle safety from rollover. The NMPC controller is developed using CasADi tools in the MATLAB environment. The CasADi tool provides a platform to formulate the NMPC problem using symbolic expressions, which is an easy and efficient way of solving the optimization problem. In the present work, the vehicle lateral dynamics are modeled using the Pacejka nonlinear tire model. Further, a new algorithm is developed based on the box slope and box detection methods to process the stationary and moving obstacles. These methods use the vehicle\u27s current heading and generate a light detection and ranging (LIDAR) view through rectangular box regions. These box regions mimic the actual vision sensor regions, and logic can easily be applied to real vehicle conditions. From the results, it is observed that the vehicle avoids both moving and stationary lengthy obstacles and can safely navigate through a pool of obstacles that can mimic real-world off-road scenarios. Further, the simulations with Gaussian noise in vehicle state estimations and obstacle states confirmed that the developed algorithm can reach the target without collision by meeting all the vehicle safety constraints for the considered uncertainty limits

On-Track Demonstration of Automated Eco-Driving Control for an Electric Vehicle

This paper presents the energy savings of an automated driving control applied to an electric vehicle based on the on-track testing results. The control is a universal speed planner that analytically solves the eco-driving optimal control problem, within a receding horizon framework and coupled with trajectory tracking lower-level controls. The automated eco-driving control can take advantage of signal phase and timing (SPaT) provided by approaching traffic lights via vehicle-to-infrastructure (V2I) communications. At each time step, the controller calculates the accelerator and brake pedal position (APP/BPP) based on the current state of the vehicle and the current and future information about the surrounding environment (e.g., speed limits, traffic light phase). The target vehicle is a Chevrolet Bolt, an electric vehicle, which is outfitted with a drive-by-wire (DBW) system that allows external APP/BPP to command the speed of the vehicle, while the operator remains in charge of the steering wheel. The DBW is connected to a rapid prototyping unit by dSpace. This unit includes: (1) real-time software that gathers all digital and analog sensors, as well as signals from the CAN bus; (2) a simple digital twin representation of the track; and (3) automated driving controls. The digital twin representation includes virtual stop signs, speed limits, and traffic lights. The digital twin can broadcast information about current and future road environment (e.g. SPaT) based on the actual position of the vehicle on the track, and correlate that to a position in the digital twin. The automated driving controls include eco-driving controls and an additional safety-focused control layer. The experiments include five road scenarios, and three control calibrations, and each combination is repeated three times. The road scenarios are all within 3.7 km in length, corresponding to one full loop around an oval track at the American Center for Mobility in Michigan, and feature various combinations of stop signs, traffic signals, and speed limits. The control calibrations correspond to a human-driver-like baseline, non-connected automated driving, and automated driving with V2I connectivity. Test-to-test variability is within 2%, thanks to careful thermal conditioning of the vehicle prior to tests. Functionality is verified and demonstrated: no excessive jerk and no violations of traffic laws occur. Energy savings of up to 7% are demonstrated in the no-connectivity case, and up to 22% in the V2I connectivity case. These tests demonstrate the real-world energy-saving potential of automated eco-driving controls

Flexible Architecture for Testing Connected Vehicles in Realistic Traffic

Connected vehicles have the potential to transform the way we commute and travel in a multitude of ways. Vehicles will cooperate and coordinate with each other to solve problems appropriate for the environment in which they are operating. In this paper, we focus on the development of test equipment that includes the infrastructure and vehicles to measure and record all of the information necessary to quantify the performance of cooperative driving algorithms in realistic scenarios. The system allows tests to include real vehicles on the track and virtual vehicles in a digital twin. Real and virtual vehicles interact through the road-side units and test facility network, allowing each test vehicle to receive messages from virtual vehicles as well as the infrastructure. Messages transmitted from the test vehicles are received in the digital twin, allowing the real vehicle to interact with virtual vehicles. This provides the capability to test algorithms in congested traffic without the expense and risk of conducting tests with many cars. The system is shown to allow for real-time operation of connected vehicles in closed loop operation using industry standard networks, along with a protocol for centralized traffic management, which is not currently standardized. Tests have been performed at highway speeds. The architecture has a low barrier to entry application programming interface for its vehicle to infrastructure network that utilizes the Robotic Operating System interface. The paper describes the development and integration of components and protocols, characterization of the network performance, methods for recording data referenced to a single clock, and demonstration of the repeatability of measurements made on test vehicles. The discussion at the end of the paper looks at current research on the impact of cooperative driving algorithms on energy efficiency and traffic flow