Now that the importance and significance of autonomous driving are known, it is now time to perform an in-depth analysis and identify the companies that have already begun the phase of building this concept from scratch.
Waymo's heavy focus is on the Level 5 simulation (check out the previous blog to know more). Around 25,000 cars are currently running round-the-clock and have traveled over 4.4 billion kilometers in total.
The disengagement ratio is 0.18 per 1600 kilometers (as per 2019). Disengagements are the number of times the vehicle needs human intervention to make changes. The ratio is, therefore, significant when it comes to the autonomy of the vehicle as a lower number will suggest a more self-reliant vehicle.
The vehicle has been tested under extreme weather conditions and also under emergency scenarios. It has also been tested by making it interact with law enforcement and first responders.
Waymo has unlocked opportunities for people with physical challenges by using Braille labels, accessible rider support, audio cues, and other accessibility tools.
The project, first initiated back in 2009, is now aiming for ride-sharing in certain, if not all, areas. It has access to all of Google's resources.
The above shows an example of how the on-board instruments look when performing. As discussed earlier, every component performs its task diligently and is monitored by sub-systems that ensure that they work as desired.
The flowchart for the call-to-action for the vehicle is a solution to four key questions:
The marked color codes are for traffic, signs, and pedestrians on the road. The paths marked are the predictions made by the software as to how the external factors would behave after some amount of time has passed.
Cruise is GM's zero-emission self-driving car and has been tested in metropolitan environments of San Francisco and New York. By the year 2017, Cruise has covered over 212,000 kilometers with a disengagement ratio of 0.8 per 1600 kilometers.
GM has a trait called "Express Drive" that allows ride-share drivers to rent quality vehicles for extended periods at a great value.
CEO Mary Barra quotes that the vision of this project is simply "zero crashes, zero emissions, zero congestion" and wants people to think that GM is more of a tech-based automotive company. The vehicle has been built from the ground up and has deployed self-driving cars as a commercial service in 2019.
The main reason for such huge investments by other companies is that General Motors has valuable experience spanning over a century. So, unlike other companies that buy the vehicle from others, GM builds its autonomous cars all by itself, making it more convenient for the vehicle to out-perform its competitors.
Uber is known to the world as a successful ride-sharing company that can transport people from point A to point B with just their smartphones. It has in-house AI research and has partnered with Volvo and Daimler, with Toyota being the prime investors by spending around $500 million.
Unfortunately, due to an accident involving a pedestrian's untimely death during field testing in 2019, this project has come to a somewhat halt.
Tesla is the game-changer in this sector, bringing in all-electric autonomous drives that have shifted the outlook of the industry, being so relatively new. With over 12 ultrasonic sensors, the founder and billionaire Elon Musk believe that the LIDAR component is not needed. Instead, it is equipped with highly sensitive cameras which are managed by the autopilot system.
The high focus is on vision/camera, and there is internal research being conducted to perfect artificial intelligence. Even though it struggled after splitting up with Mobileye, Tesla now has just under 300,000 vehicles on the road.
Since it is software-defined, Tesla can make suspension and other changes to the chassis via the on-board software, making it far more efficient than any other company. Tesla's approach is to "enable your car to make money for you when you aren't using it".
The above illustration portrays the functionality of the autopilot feature of the vehicle and how it detects ongoing traffic using its sensors.
The above tabulation shows how companies with no recorded background in the automotive industry have shown an increased amount of interest in the AV ecosystem. This is because this sector of the industry has shown a tremendous amount of potential in the future.
The primary concern for employers is that there is a sizable skill gap in the people that apply for jobs in this sector.
The top skills that are required are actually programming languages like C, C++, Java, Python, MATLAB, etc. Only after the individual is thorough with those languages, they will be considered to be hired. The engineering background comes secondary in consideration in this field.
A common myth that demotivates the engineers is that programming languages aren't easy to master and have complex logical reasoning. The reality is that even basic knowledge of programming is more than enough to manipulate and upgrade the already designed algorithm for the vehicle's performance.
The above snapshot is a summary of the job openings posted by the companies. The one skill that is immanently required by employers is a background in programming and machine learning.
Applicants who do not have experience working with complex software inputs can enroll themselves in courses that provide a basic idea of programming.
Before such technology is brought forth into the market on a regular basis, it is crucial for companies to test the genuineness of the vehicles. As such, thousands if not millions of tests must be conducted with the main focus on prediction, scene understanding, and social interactions of the vehicle.
For this procedure, more technology-based companies will be interested in moving closer to the possibility of vehicles mimicking human behavior on a level not yet achieved. As a result, they will be providing more opportunities for engineers that want to pursue autonomous driving as a career.
Overall, the future holds an entirely new section of job openings where big data and data analytics companies will drive the new in-car technology and services.
The recent breakthrough in the automotive industry is set to provide many career opportunities to engineers. As discussed earlier, autonomous driving is entirely software-based. Hence, engineers who want to make it big in this field must develop skills that adhere to those needs.
Skill-Lync offers programs that give you the proper and updated knowledge of autonomous driving. Start weaving your path to a successful career by enrolling for Skill-Lync's courses today.
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