AUTONOMOUS VEHICLE - Directional Control

INTRODUCTION

Autonomous vehicles, self-driving robocars, have been a dream in the science fiction literature for over 60 years. The last three decades, the research efforts have steadily increased to make this dream come true. Recent advancements in sensor technology and the increase of processing power of embedded systems have further fueled the development. A major milestone, proving the potential of autonomous driving, was the DARPA Grand Challenges, held in 2004 and 2005 . Numerous challenges and competitions have been held since then, and various automobile manufactures have demonstrated advanced systems more or less capable of autonomous driving

An autonomous vehicle system is often divided into several subsystems handling tasks such as sensing the environment, estimating the state of the vehicle, planning a path and a trajectory, and finally controlling it in a safe and comfortable manner

 

Control Loop

What is a Control Loop ?

A control loop is a system for managing a process that keeps a variable at a particular set point.

The loop's individual steps work in concert with one another to control the system. The control loop functions in four steps after the set point has been determined.

Parameters for self driving cars

Sensors The sensors mounted on the vehicle, such as RADARs1 , LIDARs2 , RTKGPS3 , IMUs4 , cameras, etc., collect information about the vehicle and the surrounding environment.

State Estimation The information collected by the sensors is used to estimate the states of the vehicle and objects in the surrounding environment.

Planner The planner computes a path or trajectory from point A to B for the vehicle to follow and sends it to the controller.

Controller The vehicle controller is often divided into one for longitudinal control and one for lateral control and can consist of a combination of high-, mid-, and low-level controllers too.

Vehicle The autonomous vehicle itself.

 

Sensors

Sensors used in lateral control

There are many sensor used in lateral control of autonoumus vechicles but mainy it uses vision sensors.

1. Pixel Counter Sensor

 In order to measure an object, pixel counter sensors count each pixel in an image that has the same grayscale value.

2.3D Sensors

 The surface and depth of an object are both scanned by 3D sensors. In other words, these sensors can be used to assess the contents of the packaging and identify a product based on its size.

3. Match sensors

 it is  used to check the location of labels on packages, to compare patterns like inscriptions to reference patterns, or to ensure that a weld nut is in the right place.

 

LATERAL CONTROL IN AUTONOMOUS CARS

The Lateral Controller Stanley block can be set up in one of two ways, depending on the vehicle model used to derive the control law

Kinematic bicycle model: The vehicle is thought to have very little inertia according to the kinematic model. This design works well in low-speed settings with little to no inertial effect. Based on the vehicle's velocity, reference pose, and current pose, the steering command is calculated.

(x, y) is the vehicle’s center of mass. ψ is the current heading of the car (heading angle) and v is the speed of the vehicle. lf, lr is the distance from the center of the mass to the front and rear axle respectively. β is the angle of v with respect to the car axis(sideslip angle of the vehicle as we are taking it at CG). The front wheel steering angle is δf and the car acceleration is a. For simplicity, we assume it is a front-wheel-drive car and we will write δf as δ for now.

 

 

 

The equations now are:

Here the input variables are acceleration a (which can be negative for deceleration) and the steering angle δ

Kinematic model works is a very simple model and can work well in some controls problem. It is computationally inexpensive and also easy to parameterise- so portable.

 

 

Dynamic bicycle model:

Inertia effects like as tyre slide and steering servo actuation are included in the dynamic model. The controller can manage realistic dynamics thanks to this more intricate yet accurate model. In order to compute the steering instruction in this setup, the controller additionally has to know the path curvature, the vehicle's current yaw rate, and the current steering angle.

 

The next model in the fidelity chain is a dynamic bicycle model. Now instead of body side slip angles, we would assume tire slip angles (angle from tire speed to tire orientation). The analysis also would be in vehicle coordinates rather than global. Also the longitudinal velocity of the vehicle is u and lateral velocity is v. The yaw rate (rate of change of angular velocity around Z axis) is r and δf is the front steering angle.

angle.

 

This equation suggests that the needed steering angle for the turn has two components. The dynamic portion, which is the difference between the front and rear tyre slip angles, is known as the L/R static part, sometimes referred to as the ackermann angle. Understeer is a condition where the front tyre slip angle is greater than the rear tyre slip angle. This suggests that in order to maintain a constant radius turn at nonzero speed, the steering angle must be greater than the Ackermann angle. Oversteer occurs when the front steering angle is smaller than the Ackermann angle and the front slip angle is greater than the front slip angle. The steering angle is equal to the Ackermann angle if the front and rear slip angles are equal.Ackermann angle and the condition is termed neutral steer.

Benefits of self-driving cars

Self-driving vehicles represent a significant advancement both technologically and practically. You see, these cars are equipped with everything necessary to facilitate and speed up our daily work.

Companies that operate self-driving cars can save time and money (drivers can concentrate on more difficult jobs, for example) and even run around-the-clock, all year round.

Less accidents occur overall (AI algorithms are never tired, intoxicated, or sleepy)

Of course, we're not claiming that automated vehicles are currently in widespread usage. This initiative is still in the planning stages, in part because autonomous vehicles are illegal to drive on public roads in many nations. However, it's only a short-term issue.

The legislation will need to alter to reflect how technology is developing and spreading.

 

Application

There are various application for self-driving cars; here are a few of them:

1.      Waymo

 

It is a US-based firm that is developing the first autonomous ride-hailing service in the world as well as solutions for local deliveries and autonomous transportation. They hope to create a fully automated

driving system that can take the place of human drivers. Both passenger cars and lorries might use such a system. Using a network of radars, lidars, and cameras, Waymo developed its solution. In both the actual world and in simulations, Waymo's vehicles have now covered more than 20 billion miles.

Their systems are capable of recognizing people, bikes, and other impediments, among other things.

2.      BMW

That business is one of many developing autonomous automobiles. Did you know that the first time a BMW car—specifically, the i3—automatically parked itself in a garage occurred in 2015? BMW unveiled its Autonomous Driving Campus, where they are developing self-driving cars, three

years later. BMW may streamline their work by consolidating all of their research and development on this property.

The vehicle is thought to have very little inertia according to the kinematic model. This design works well in low-speed setting

 Conclusion

Self-driving cars will take over as the dominant form of transportation as technology develops globally. The concepts of accountability, responsibility, and effectiveness are central to the legal, moral, and social ramifications of self-driving cars.