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Creating a self-driving toy car

To build your own self-driving toy car, you don’t have to be a VC-funded startup. The car is even better if it is small, remote-controlled, and can be placed right on your desk. Our next four blog posts will cover building our

own self-driving toy car with a Raspberry Pi, a generic remote-control car, a few basic electronics components, FloydHub GPUs, and Keras deep learning framework.

Here is a quick glimpse of my desk right now, in case you still aren’t excited about our autonomous driving project:

Let’s take a look at how the future might unfold. I’ll write four posts about our journey:

  • History and overview of autonomous vehicles in part one
  • Python and Raspberry Pi: Building a custom RC car controller
  • Three; Train, deploy and implement a self-driving car model with Keras
  • Refactor, retrain, and re-deploy a model that is improved A brief history of a self-driving toy car

While interest in autonomous driving has gained momentum recently, autonomous technology has been around since the beginning of motorized vehicles.

The cruise control feature on your vehicle can automatically control your speed – for example. As early as 1950, cruise control – which has been approved by the U.S. patent office – was used in steam engines to regulate fuel use, allowing constant speeds to be maintained.

With the DARPA Grand Challenge in the early 2000s, self-driving technology was finally propelled into the modern era after a long and fascinating history.

A Challenge from DARPA

It was first held in the Mojave Desert on March 13, 2004. Autonomous cars competed for the first time in the Grand Challenge. A U.S. Department of Defense research arm, DARPA, organized the conference.

In the 1960s and 1970s, they also invented the first Internet (called ARPANET). Therefore, it’s obvious that they’re looking ahead.

 

Self-driving toy cars were the bright future in 2004

In 2014, none of the cars were able to complete the challenge, so there were no winners. On the 240 km course, Carnegie Mellon University’s Sandstorm traveled the most; it covered 11.78 kilometers.

The second DARPA competition took place in 2005. October 8th was the date of the race. The participation rate was almost 100% higher than last year – and five cars completed the race.

In November 2007, DARPA held its third Grand Challenge, known as the Urban Challenge. With the Urban Challenge, DARPA decided to take things to the next level. A predefined route was specified for cars in previous challenges.

The cars were required to drive because this time they had to follow all the regular rules and regulations of driving on urban roads. Despite being extremely challenging, a few teams completed the race.

Attention to business

An important turning point in the history of self-driving technology was the Urban Challenge. Therefore, commercial attention was piqued (including interest from venture capitalists).

Google

A Google autonomous car was first developed in 2009, according to the company. In March 2018, a woman died in an accident in Tempe, Arizona. Uber recently regained testing permission for its self-driving toy car.

Unfortunately in Tempe, Arizona in March 2018, a fatal accident killed a woman in spite of having a human driver in the vehicle.

Under a new ruling, Uber is now permitted to test autonomous vehicles as long as it keeps speed limits at 25 mph and has two human passengers with it at all times. The Volvo SUVs being used by Uber are equipped with instant braking systems. Pittsburgh will soon begin testing.

Traditionally manufactured cars

In 2013, most major automakers, including General Motors, Mercedes-Benz, Ford, and BMW, had made it clear that they were also developing self-driving automobile technology.

Uber

Uber, a ridesharing company, has started developing self-driving cars in Pittsburgh. In Uber’s self-driving toy car prototype, sensors including radar, lasers, and cameras were mounted on the roof to collect traffic data and map the road.

Unfortunately, a fatal accident in Tempe, Arizona in March 2018 took the life of a woman regardless of the fact that the car was driven by a human being. Uber was now permitted to test self-driving cars, as long as they complied with a speed limit of 25 mph and had at least two human drivers on board.

The Volvo SUVs being used by Uber are equipped with instant braking systems. In Pittsburgh, testing is about to begin.

Lyft

As a ride-sharing rival to Uber, Lyft has started testing self-driving toy cars. Their campus is located in Contra Costa County, California, not the same as Uber’s.

Generally, Uber’s effort to create a self-driving fleet has been more partnership-oriented. In addition to GM, Ford, Aptiv, Drive.ai, Waymo, and Jaguar, they have partnered with a number of other companies as well.

Tesla

Tesla claims to be ahead of everyone in the game. It’s the first successful U.S. car startup in decades. Tesla’s CEO Elon Musk announced recently that the company may have a self-driving toy car on the market by this year:

Neither Tesla nor any of the other carmakers are likely to surpass the company in self-driving or in getting fully automated vehicles to work on their own. They’re just poor software engineers. And this is a software problem.”

A self-driving toy car is being announced in more and more projects

More and more companies continue to announce their own self-driving toy car projects in what often feels like a drag race for self-driving hegemony.

 

Yandex, the Russian search engine giant showcasing its self-driving toy car technology at the 2019 Consumer Electronics Show in Las Vegas, demonstrated its technology a few weeks ago. A number of self-driving cars have been tested on the rugged streets of Russia by Yandex.

A safety engineer is leading all self-driven rides so far. The company claims to have done more than 2,000 of them so far.

Self-driving toy cars at different levels

In any detailed report about progress in autonomous vehicles, you’ll inevitably hear about one important term in the self-driving lexicon. According to the levels first defined by SAE International (Society of Automotive Engineering) in 2014, autonomous vehicles may have up to three levels of autonomy.

Level 1

Assistance to the driver is limited. The system that can control steering and acceleration/deceleration individually, but not both simultaneously, in specific circumstances.

Level 2

The steering and acceleration of a vehicle are controlled by driver-assist systems. While these systems reduce the human driver’s workload, they still mandate that person’s attention be maintained at all times.

Level 3

Autonomous vehicles can navigate traffic on divided highways or other situations by themselves. The user is not required to intervene when the system is operating autonomously. In situations when the vehicle is forced to exceed its limits, a human driver must take over.

Level 4

Automobiles are mostly self-driving but may need a human driver in some circumstances.

 

 Level 5

Totally independent. Level 5 vehicles are capable of self-driving at any time and under any condition. The controls do not need to be manually operated.

Progress made to date

Few companies have attained Level 2 status as of 2016. These include:

  • Autopilot from Tesla
  • Pilot Assistance System Volvo
  • Drive Pilot for Mercedes-Benz

Using its AI traffic jam pilot, Audi claims its new A8 will be the first vehicle to achieve level 3 autonomy. Since mid-October 2017, Waymo’s level 4 autonomous cars have been testing in Arizona. The rest of these commercial efforts will progress through these levels of self-driving toy cars in the coming months and years.

Simulators are what we will discuss

In addition to their cars have covered more than 5 million miles on highways, Waymo’s simulators have covered billions of miles. This is one of the main reasons why Waymo is far ahead of most companies in the field. In 2016,

Waymo logged more than 2.5 billion virtual miles in its car simulator Car Craft. It is possible to simulate thousands of different scenarios and maneuvers in Car Craft every day. In addition to testing scenarios that move simulated self-driving cars around Austin, Mountain View, and Phoenix, there are still 25000 simulated self-driving cars in full-scale runs.

To validate even a 20 percent improvement over humans, the RAND Corporation estimates it would need to drive 11 billion miles. To travel this distance via 100 cars, would take 500 years of non-stop driving. These problems can be solved with simulations.

Software developers can use them to test and validate the performance of self-driving hardware without risk of harm to themselves. The DRIVE Constellation simulator platform was opened up for partners in September 2018 so they could integrate their world models, vehicle models, and traffic scenarios.

 

Autonomous vehicles and ethics

In order to understand self-driving toy cars, we must discuss the ethical issues that surround their development before going on to the technology. In her excellent guide to AI Ethics Resources, Rachel Thomas of fast.ai has these points to note:

We should all be thinking about ethics in our work.

Artificial intelligence that drives itself, in particular, raises a lot of ethical questions. Driving is a continuous stream of decisions, as anyone who has driven a car can attest to. You need to follow the laws of the road, while also maintaining an eye out for other drivers and pedestrians, as well as being ready for any surprises, such as inclement weather.

This very ethical dilemma has been debated many times since Uber’s accident in Tempe that killed a pedestrian. The road can be murky at times, especially during intersections, turns, and crosswalks. Since these decisions will have a huge impact on how our cars of tomorrow behave, this is a momentous event in AI. The algorithm becomes the policy.

Vehicles that drive themselves will soon be available. There can be no doubt about this. As a result of many years of evolution, humans are better at mechanical tasks and have a greater sense of perception.

An automated system must put in some time and effort to perform as well as a human. Prior to self-driving toy cars replacing human drivers, there needs to be a solution to this ethical issue.

Self-driving cars: the technology

In this blog post series, we’ll be building our own toy self-driving car, so let’s take a closer look at the technology behind self-driving.

Self-driving toy car has a typical architecture consisting of two parts:

  • In perceiving the world
  • The system that makes decisions.

Many subsystems are associated with the perception system, including:

  • Localization of autonomous vehicles
  • Creating a static obstacle map
  • Tracking and detecting moving obstacles
  • A map of the road

Detecting and recognizing traffic signalization

Perception system sensors also enable determining the current state of the vehicle (position, speed, direction, etc.) at any given time. Usually, decision-making systems are made up of many subsystems, each of which performs a specific task, such as:

  • Planning your route
  • Planning a route
  • Choosing behaviors
  • Organizing motions
  • Management

In addition, the decision-making system plays a role in deciding how to maneuver the car between positions based on the current situation and the traffic rules. This decision-making system must be aware of the car’s position and its environs in order to perform this action.

In order for the decision-making system to work properly, a location must be provided by the Localizer module. Platform geometry, offline maps, sensor data, and offline maps are all used to calculate the car’s position.

Mapper uses offline maps and online occupancy grids data to produce a merged map composed of information present in offline maps and the current state.

A Route Planner calculates the route from the desired starting position to a destination. A set of paths is then computed by the path planner. Paths are collections of poses, while routes are collections of waypoints. Poses define coordinate pairs in Offline Maps, and the car’s orientation relative to these coordinate pairs.

The behavior Selector module chooses whether to keep lanes, handle intersections, or handle traffic lights. The Motion Planner module computes the optimal path from a car’s current state to the target, following the route

defined by the Behavior Selector module, meeting the kinematic and dynamic constraints of the car, and providing passengers with comfort.

To avoid collisions, the Motion Planner provides the trajectory to the Obstacle Avoided module, which corrects it (generally by reducing the velocity) if necessary. In the last step, the Controller module receives the Motion

Planner trajectory which will eventually be modified by the Obstacle Avoider and processes it to calculate and send commands directly to the steering wheel, throttle, and brakes actuators, thereby executing the modified trajectory as efficiently as possible.

 

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