The Future On Your Way: Self Driving Car

    The Future On Your Way: Self Driving Car

    

What is Self-Driving Car?

          A Self-driving vehicles (cars, bikes and trucks) in which human drivers are never required to control the vehicle or that work without human intervention to predetermined destination over roads and this known as Smart or “driver less” Vehicles. It is combination of sensors, Camera, Navigation System, Artificial Intelligence (AI) and software to control and drive the vehicle Safely.

Companies that are developing the driver-less car.

Many major automotive manufactures including Ford, Mercedes Benz, General Motors, Volkswagen, Audi, Toyota, Volvo, BMW and Nissan are in the process of testing driver-less car system. BMW has been testing driver-less systems since around 2005.

SAE International Releases Updated Visual Chart for Its “Levels of Driving Automation” Standard for Self-Driving Vehicles:

which range from Level 0 to 5. Let’s take a brief look at each stage. 

• Level 0 does not feature any self-driving tech at all. 
• Level 1 cars offer at least one system that helps the driver brake, steer, or accelerate, but if there are multiple systems, they are not capable of communicating with each other. 
• Level 2 cars can simultaneously control steering and speed, even if the driver is not driving, for short periods of time. Think lane-centring technology combined with advanced cruise control, as an example. 
• Level 3 vehicles are fully autonomous but require driver attention. These cars aren’t yet available but are being tested by some tech start-ups.
• Level 4 cars, once programmed to a destination, will not need driver input, but the controls are available should the driver wish to intervene.
• Level 5 cars will be fully autonomous without any driver input.

Core Technologies Used in Self Driving Cars.

Cameras

Cameras used in self-driving cars have the highest resolution of any sensor. The data processed by cameras and computer vision software can help identify edge-case scenarios and detailed information of the car’s surroundings. 

All Tesla vehicles with autopilot capabilities, for example, have 8 external facing cameras which help them understand the world around their cars and train their models for future scenarios. 

Unfortunately, cameras don’t work as well when visibility is low, such as in a storm, fog or even dense smog. Thankfully self-driving cars have been built with redundant systems to fall back on when one or more systems aren’t functioning properly. 

ADAS System

ADAS stands for Advanced Driver Assistance System and it's a technology which has been developed for safer driving. Originally designed for driver-less vehicles, it's a system which uses cameras and sensors and a sophisticated algorithm, to notify the driver of a potential problem. This might be a weaving cyclist, a stopped vehicle in the road, drifting across lanes, or sudden braking of the vehicle in front. All these hazards can be instantly communicated to the driver, allowing them to take the necessary action avoid accident.

GPS

However, an image of the surroundings is not enough to drive safely around the streets of a city. A GPS system is also present to help the car position and navigate itself. But the accuracy of commonly available GPS is about 4 meter RMS.

LiDAR and RADAR

Therefore, in order to improve the accuracy of navigation, it also uses a set of sensors such as Light Detection and Ranging, commonly referred to as LiDAR. A LiDAR works by measuring distance to a target by illuminating the target with pulsed laser light and measuring the reflected pulses with a sensor. Differences in laser return times and wavelengths are then used to make digital 3-D representations of the target. A LiDAR can give an accuracy of up-to 2.5 cm. Multiple LiDAR modules throughout the body of the car help in creating an accurate map of the entire surroundings and avoiding blind spots. LiDAR and RADAR play an important role in collision avoidance as well. 

LiDAR can detect micro-topography that is hidden by vegetation which helps archaeologist to understand the surface. Ground-based LiDAR technology can be used to capture the structure of the building. This digital information can be used for 3D mapping on the ground which can be used to create models of the structure.

Other Sensors 

Self-driving cars will also utilise traditional GPS tracking, along with ultrasonic sensor and inertial sensors to gain a full picture of what the car is doing as well as what’s occurring around it. In the realm of machine learning and self-driving technology, the more data collected.

Using the concepts of Transfer Learning, a pre-trained model can be modified for the purposes of detection of different kinds of objects and their classification. This functionality is very important in the real-world autonomous navigation by a vehicle.

Deep learning using Convolution Neural Networks (CNNs) is being used to detect and classify the traffic lights which can convey the important navigation information to an autonomous vehicle.

One more area where deep learning is being used in autonomous vehicles is the identification of the lanes at pixel level using Fully Convolutional Networks (FCNs). This helps in making sure that all the lane and traffic rules are followed by an autonomous vehicle.

How self-driving cars work.

AI technologies power self-driving car systems. Developers of self-driving cars use vast amounts of data from image recognition systems, along with machine learning and neural network, to build systems that can drive autonomously.

The neural networks identify patterns in the data, which is fed to the machine learning algorithms. That data includes images from cameras on self-driving cars from which the neural network learns to identify traffic lights, trees, curbs, pedestrians, street signs and other parts of any given driving environment.

For example, Google's self-driving car project, called Waymo, uses a mix of sensors, LiDAR (light detection and ranging -- a technology similar to radar) and cameras and combines all of the data those systems generate to identify everything around the vehicle and predict what those objects might do next. This happens in fractions of a second. Maturity is important for these systems. The more the system drives, the more data it can incorporate into its deep learning algorithms, enabling it to make more nuanced driving choices.

How the vehicle travels from one location to other location?

·        The driver (or passenger) sets a destination. The car's software calculates a route.

·        A rotating, roof-mounted Lidar sensor monitors a 60-meter range around the car and creates a dynamic 3D map of the car's current environment.

·        A sensor on the left rear wheel monitors sideways movement to detect the car's position relative to the 3D map.

·        Radar systems in the front and rear bumpers calculate distances to obstacles.

·        AI software in the car is connected to all the sensors and collects input from Google street view and video cameras inside the car.

·        The AI simulates human perceptual and decision-making processes using deep learning and controls actions in driver control systems, such as steering and brakes.

·        The car's software consults Google Map for advance notice of things like landmarks, traffic signs and lights.

·        An override function is available to enable a human to take control of the vehicle.

What are the Challenges with Autonomous Cars?

The challenges range from the technological and legislative to the environmental and philosophical.

Lidar and Radar

Lidar is expensive and is still trying to strike the right balance between range and resolution. If multiple autonomous cars were to drive on the same road, would their LiDAR signals interfere with one another? And if multiple radio frequencies are available, will the frequency range be enough to support mass production of autonomous cars?

Weather Conditions

What happens when an autonomous car drives in heavy precipitation? If there’s a layer of snow on the road, lane dividers disappear. How will the cameras and sensors track lane markings if the markings are obscured by water, oil, ice, or debris?

Traffic Conditions and Laws

Will autonomous cars have trouble in tunnels or on bridges? How will they do in bumper-to-bumper traffic? Will autonomous cars be relegated to a specific lane? Will they be granted carpool lane access? And what about the fleet of legacy cars still sharing the roadways for the next 20 or 30 years?

Artificial vs. Emotional Intelligence

Human drivers rely on subtle cues and non-verbal communication like making eye contact with pedestrians or reading the facial expressions and body language of other drivers to make split-second judgement calls and predict behaviors. Will autonomous cars be able to replicate this connection? Will they have the same life-saving instincts as human drivers?

Predicting agent behavior: It’s currently difficult to entirely understand the semantics of a scene, the behavior of other agents on the road and appearance cues such as blinkers and brake lights. Not to mention, predicting human error such as when a person signals a left turn but actually turns right.

Understanding perception complexity: Self-driving vehicles fail when objects are blocked from view such as during snowstorms, objects viewed in a reflection, fast moving objects around a blind spot and other long-tail scenarios.  

Cyber security threats: Software is written by humans, and humans write code with vulnerabilities. Although very few people understand neural networks well enough to exploit these vulnerabilities, it can and will be done.

Continuous development and deployment: One problem facing self-driving vehicles is the process of re-validating changes to the software. If and when the code base changes, does this require testing for another 275 million miles to validate performance?

The future of self-driving cars

Despite the definite problems, self-driving car companies are moving forward and improving every day. 

Considering an estimated 93% of car accidents are caused by human error, the opportunity for self-driving cars to remove a major threat in the daily lives of billions of humans is too great to pass up. There will be many debates over the efficacy of self-driving cars as well as regulatory hurdles before we see Level 5 autonomy deployed globally.

Advantages of Driver-less Cars

1.   Travelers would be able to journey overnight and sleep for the duration.

     

2. Speed limits could be safely increased, thereby shortening journey times.

3. There would be no need for driver’s licenses or driving tests.

4.Presumably, with fewer associated risks, insurance premiums for car owners would go down.

5. Efficient travel also means fuel savings, simultaneously cutting costs and making less of a negative environmental impact.

6. Greater efficiency would mean fewer emissions and less pollution from cars in general.

7. Reduced need for safety gaps, lanes, and shoulders means that road capacities for vehicles would be significantly increased.

8. elf-aware cars would lead to a reduction in car theft.

9. Passengers should experience a smoother riding experience.

10. Difficult manoeuvring and parking would be less stressful and require no special skills. The car could even just drop you off and then go park itself.

11. Human drivers notoriously bend rules and take risks, but driverless cars will obey every road rule and posted speed limit.

12. Entertainment technology, such as video screens, could be used without any concern of distracting the driver.

Disadvantages of Driver-less Cars

 1. A self-driving car would be unaffordable for most people, likely costing over $100,000. 

2. Self-driving cars would be great news for terrorists, as those vehicles could be loaded with explosives and used as moving bombs.

3. As drivers become more accustomed to not driving, their proficiency and experience will diminish. Should they then need to drive under certain circumstances, there may be problems.

4.  if the car crashes without a driver, whose fault is it: the software designer or the owner of the vehicle? Driverless systems will definitely trigger many debates about legal, ethical, and financial responsibility.

5. Human behaviour such as heavy foot traffic, jaywalkers, and hand signals are difficult for a computer to understand. In situations where drivers need to deal with erratic human behaviour or communicate with one another, the driverless vehicle might fail.

6. Reading road signs is challenging for a robot. GPS and other technologies might not register obstacles like potholes, recent changes in road conditions, and newly posted signs.

7. The road system and infrastructure would likely need major upgrades for driverless vehicles to operate on them. Traffic and street lights, for instance, would likely all need altering.

8. Hackers getting into the vehicle's software and controlling or affecting its operation would be a major concern.

9. Truck drivers, taxi drivers, Uber/Lyft, and other delivery people will eventually lose their jobs as autonomous vehicles take over.