For the past few years, autonomous vehicles have received an unprecedented welcome. Every automobile manufacturer is exploring ways to get in the business of autonomous vehicles as per their own capacity. While some people may consider the term “autonomous vehicles” to be the latest, but for technology enthusiasts, it is something that has been in the research phase for decades. In this lieu, the first practical approach was taken by the researchers of Stanford University by building a Stanford cart in 1961. This first autonomous cart like vehicle was capable of navigating through certain obstacles by way of the cameras and early developed AI algorithms. But it used to take about 10 to 15 minutes for the cart to move 1 meter.

Now you may be wondering about the time it takes to move 1 meter. But, in today’s reality, a lot of work is being done on the technological front. Therefore, it seems very absurd in today’s time to wait for such a long span to take a lap of just 1 meter. As compared to the past, the AI algorithms have improved a lot, design tools are in abundance, and research methods have been enriched with a lot of utilities. All these improvements pave the way for better autonomous vehicles. This is why, technological giants like Tesla, Uber, Google, Ford, and General Motors are in fierce competition to build effective and safe autonomous vehicles.

Building Blocks of an Autonomous Vehicle

There are a lot of technologies that serve the purpose of automation in different sectors. For example, programming brings automation in the software, while Programmable Logic Controller (PLC) brings automation in industrial machines. But since the automotive sector is a completely different regime and autonomous vehicles take into account different technologies. Therefore, it is better to say that the building of autonomous vehicles involves cross-collaboration of different building blocks having their own respective methodologies and working mechanisms. Following is the detail introduction to all those basic building blocks that are mandatory to build an autonomous vehicle:

Perception

Perception in the world of autonomous vehicles is not the same as we perceive in the literal world. The concept of perception here uses the rich combination of high-tech cameras and sensors to get real-time data of the objects. As the first line of force, reliable and around the clock operations of perception is very crucial in the autonomous vehicles. The reason lies in its importance in the core decision making pertaining to different autonomous functions that an autonomous vehicle is supposed to perform. There are multiple perception elements, such as Radars, LiDARs, and a combination of certain cameras. Now one may question that since getting data is the main goal of perception and these requirements are very well being fulfilled by LiDARs and Radars, then why are cameras used? Basically, to get the in-depth information combinations of cameras, radars, and LiDARs, they are used in a process known as Sensor Fusion to not only label the objects but to confirm them too. For example, it is highly possible that radar may identify a body that is in front of the autonomous car and is moving with X velocity in the Y direction. But the camera will confirm whether this moving object is a car, cyclist, a pedestrian. This confirmation is very important from the perspective of autonomous vehicles, as the decisions are highly dependent on the type and nature of moving objects.

Interpretation

While perception is a sort of sensory component of the autonomous vehicle that senses the external parameters, interpretation is related to the translation of that sensing information into a sort of interpretive information. This is where algorithms like line following and obstacle detection are formulated. Interpretation is the software-based building block, and it is where intelligence will be embedded into the autonomous driving system. For example, when the perception stage has sensed an obstacle, then this gathered information is given to the interpreter. The interpreter will interpret this information and activates the given algorithm, which in this case, will be an obstacle avoidance algorithm.

Steering Model

This block or phase of an autonomous vehicle is a sort of an action phase. It is where practical demonstration of perception got from the sensors and the interpreted information comes into play. The steering model in the autonomous vehicle determines the angle at which the vehicle will steer left or right or maintain its position on the road. The basic driving force is the path following algorithm where steering is triggered at a specific angle to maintain position or move as per the defined settings. The steering is triggered with the help of a servo motor, which transmits the steering force via belt and pulley.

For example, imagine the sensing system senses the left turn sign on the road. It passes on this information to the interpretation that interprets the fed information in the form of the language the steering model understands. In accordance with the fed information, steering will be triggered at a certain angle to perform the left-turn action.

Control System

The control system is considered the heart of the autonomous vehicle. While the other three blocks build the platform, the control system makes sure that an autonomous vehicle gets driven on these platforms. Since constant feedback is being fed to the system to drive the vehicle up to the optimal conditions, a closed-loop control system is installed in the autonomous vehicles. Major components of the autonomous vehicle’s control system are acceleration, deceleration, and emergency braking systems. To ensure the safe and flawless driving of the autonomous vehicle, information such as deviation from the path, distance from the destination, obstacle, type of road, and any turning requirement are constantly being fed to the system, which then controls and operates the vehicle in an autonomous manner.

Conclusion

From the general perspective, autonomous vehicles may seem just an advanced version of today’s cars. But in reality, several complex processes not only make them better than today’s cars, but they also open a whole new paradigm of mobility. With the algorithms like path following, obstacle avoidance, and sheer adherence to road signs, road accidents can also be avoided. About 90 people die every day in the USA just because of road accidents. Human error is the main reason for road accidents. These 90 lives can be saved daily by adopting an intelligent and smart mobility mode, i.e., Autonomous Vehicles. With such a highly sophisticated and intelligent process of perception, interpretation, smart steering, and control system, we can stay assured that human errors can totally be negated. Because unlike humans, machines do not yawn and don’t get tired rigorously.

References

1. https://eldorado.tu-dortmund.de/handle/2003/38044
2. https://www.wired.com/story/guide-self-driving-cars/
3. https://www.sweeneymerrigan.com/car-accident-statistics-in-the-united-states/

Autonomous vehicles are becoming a hot topic with every passing day. From the safety to their control mechanisms, everything is in fierce debate. Several articles and research studies have been conducted in favour and opposition to autonomous vehicles. Apart from all these discussions, it is interesting to see different articles saying that autonomous vehicles are surely going to take on the world. According to the predictions of IHS Automotive, there will be 21 million autonomous vehicles on the road by the year 2035. The figure of 21 million advocates for the viability of autonomous vehicles. It seems as those opposing and challenging this technology shall soon settle down to rest. The figure of 21 million also narrates that the future autonomous vehicles will be intelligent and safe enough to attract car buyers to drive AVs.

Nevertheless, of all the advocacies and oppositions, one thing that has emerged as the potential clarification point is the steering control system for autonomous vehicles. It is the most crucial and important part of autonomous vehicles. If a sound explanation of the steering model and control is provided, then it will be much easier for the general public to understand its highly dynamic and intelligent mode of operations. Let’s find out how do the steering model and control work? Apart from its practical demonstrations, what shape does it have on the theoretical front?

Theory of Steering Model and Control

Most of today’s cars are equipped with the Motor-Driven Power Steering (MDPS). This system reduces the driver’s effort, as it eases the torque with the help of an electric motor. But autonomous vehicles don’t have any drivers and do not need to ease such efforts. So, this system is not recommended to be used in autonomous vehicles. For the autonomous vehicle, such a system is required that does not only actuate the steering control but should also compensate for the error. In simpler words, a steering controller along with some feedback mechanism is required.

Before getting into the detailed theory of the steering model and control, lets first have a brief look over the hardware and the basic steering model. The steering model of an autonomous vehicle is equipped with an actuator that transmits the torque to the steering using pulley and belts. It also has a potentiometer to determine the position of the motor shaft. This assembly of actuators and sensors communicate with each other with the help of RS232 and Controller Area Network (CAN). The CAN bus is used to read the position and speed of the motor. The same route is used to write the desired Pulse Width Modulation (PWM) signal so as to actuate steering accordingly.

Since we are done with establishing the basic model of the steering model, now let’s discuss the control strategy in detail. The steering control system is basically a path tracking system that controls the vehicle’s steering based on its current position and degree of deviation from the reference path.

A path tracker system can be called as the brain of the steering control and it is what locates, analyses, determines, and actuates the steering. This system works with three basic modules namely; velocity planning module, look-ahead distance module, and the path tracking module. The velocity module is responsible for planning the vehicle’s velocity with the help of a path’s curvature, side friction factor, and super-elevation. The look-ahead distance module assesses the look-ahead distance based on the vehicle’s velocity. Both of these modules set the desired point in conjunction with the reference point. Based on this, the path tracking module generates the steering angle, which is then actuated with the help of motors, connected with the vehicle’s steering model.

The whole control system is modelled and designed on the basis of a closed-loop feedback control system and has a steering controller, which with the help of the feedback loop, makes sure that the actuator follows the reference point.

Challenges

Since nothing in this world can reach ultimate perfection, therefore the same exists with the steering model and control of autonomous vehicles. Though, technology has reached a point where autonomous vehicles are no more a dream. But some challenges are still associated with the steering model and control.

The foremost challenge is to convince people about the safety of autonomous vehicles. Specifically, if we talk about the steering model and control, then the fundamental challenge is the unavailability of the contingency plan. The fundamental question is what will happen if such a sophisticated and advanced control system fails? This vulnerability becomes even more concerning in the case of human-less driving scenarios. So, the main challenge is the inclusion of redundancy.

The second big challenge is more of a design-related issue. But still, it is rich enough to be counted as the challenge to the steering model and control. Since AVs do not require any kind of human intervention, so designers and researchers are confused about whether to include the steering wheel in the future autonomous vehicles or not. This challenge is also gross from the technical point. This challenge poses a very legitimate question that even if steering wheels are removed, what design and operational consideration would be made in regard to steering model and control as well as the overall design of the AV?

Conclusion

The enrichment of artificial intelligence and machine learning algorithms is considered to be the founding stone of autonomous vehicles. It is true from the technological perspective but what if algorithms are developed and the actuators or controllers which are going to realize them, aren’t mature? This is a very concerning issue for both autonomous vehicle manufactures and enthusiasts. With the proper introduction to the theory of steering model and control, it is much easier to advocate for autonomous vehicles. The proper understanding of steering model and control enables AV supporters to convince people about the efficacy of autonomous vehicles. Likewise, challenges prepare manufacturers and designers to improve the steering model and control so as to maximize its safety. Out of all, it is now a very established fact that redundancy in the steering control must be embarked on autonomous vehicles.

Reference

1. https://eldorado.tu-dortmund.de/handle/2003/38044
2. https://www.automotive-iq.com/steering/articles/autonomous-driving-steering-concepts-self-driving-cars

Developing an autonomous driving system is one thing, and testing its efficacy is another thing. It is highly possible that researchers may have made un-realistic or ideal assumptions while designing an autonomous driving system. Such designed systems may look very promising on the paper. But since ground realities could be a bit harsh, it is possible that desired results could not be achieved. But the question arises that how can such developed systems be checked and verified? The classic approach says to run a trial test. However, given the implications associated with the autonomous driving systems, trial tests in real-time could prove to be fatal. If algorithms are not efficient and reality-based, what if it hit a pedestrian? What if it violates the traffic signals and could get in a road accident? There is a lot of risks associated with such trial tests.

While looking for ways to test autonomous driving systems in the best secure and safe environments, the development of model cars can prove to be beneficial. Since they will be a replica of fore developing autonomous vehicles, hence one can test the autonomous driving system in a much safe and cloned environment.