Summary: Autonomous driving is a hot topic that has gained immense popularity — but is it an actual concept. If yes, what are the probabilities that it brings along as it sounds oxymoron? Let’s understand the challenges of autonomous driving and how agile technology can play an important role in bringing this concept to reality.
Autonomous driving is a new concept, and everyone is excited to know about driverless vehicles. The technology is undergoing testing in several areas; the picture is not clear yet. Autonomous vehicles rely on sensors, machine learning systems, artificial intelligence technology, processors, and complex algorithms.
The self-driving car works on a map of its surroundings that is based on sensors integrated into different parts of the vehicle. The sensors and cameras detect the nearby vehicles, monitor road signs, traffic signals, pedestrian position in the surroundings to help these cars move smoothly. Lidar signals measure distance, identify lane marks to flash the light pulses. Ultrasonic sensors integrated into the wheels allow parking the car considering all the rules.
Software applications that integrate into the autonomous driving vehicles processes and execute all the sensory outputs transmit instructions to the vehicle’s actuators that control the steering and the brakes.
A lot of challenges obstruct the path of autonomous cars, and it seems like the world is still years away from experiencing it.
If you’re thinking about what Lidar is, here’s the answer.
Lidar is an abbreviation used for “light detection and ranging” and also referred to as 3D scanning or laser scanning.
Let’s see what are the challenges involved in autonomous driving.
- Setting Up Lidar Signals is Expensive
Implementing Lidar signals across multiple locations is over-expensive. But it’s an important component that must be in place to enable the right balancing of distance and range between vehicles. What if more than one autonomous car drives on the same route? And if their lidar signals would intersect each other.
- Additional Infrastructure Cost
To bring the concept of autonomous driving to life, it is important to re-build the infrastructure of the cities and metropolitan areas. It’s imperative to figure out if the autonomous cars will have trouble riding on bridges and in tunnels. And what about separate lanes for autonomous and legacy cars.
- Artificial Intelligence vs. Human Intellect
Humans rely on non-verbal interactions such as understanding the body language or reading other drivers’ facial expressions, and making eye contact with pedestrians while driving. It helps in predicting the behavior of other drivers and pedestrians for safe driving. No matter what, the autonomous cars can never assimilate the same safety instincts.
- Accountability & Responsibility During Accidents
What if the autonomous car hit an accident? Who will be responsible for the cause? Who is to be blamed — the human passenger, the pedestrian, or the manufacturer? The latest prototype reveals that the Level 5 autonomous car will not have a steering wheel or even a dashboard. It implies that if an emergency occurs, the human passenger will not be able to take control of the car.
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Should we get ready for an all-new riding experience? Or we have to wait for a few more years?
Once autonomous cars are ready to take over the roads, they can prove to be a revolutionary innovation that will transform people’s lives, not only the automotive industry. It will provide the flexibility to work while commuting. You can watch movies while on the go or can browse your social media accounts conveniently without fearing the traffic rules and regulations.
Although other industries (such as telecommunication, retail, travel) have disrupted the latest technology advancements, the automotive industry is one that has experienced a little change. However, with the launch of level 3 autonomous cars, the automotive industry has spelled magic in the area of the connected world.
The paradigm shift in technology also reveals that once the Level 5 autonomous cars are launched, the industrial giants will be left with no choice but to enter the new automotive ecosystem. A fully-immersive environment that has emerged as a result of top industry trends and innovative technology landscape.
High-Level Engineering Technologies for Autonomous Driving
The autonomous driving technology landscape can’t be built on the base of high-end concepts or blueprints. There’s a wide array of technologies, skills, and innovations involved in building one. Advanced Driver Assistance Systems (ADAS) is something that the automotive industries will require for preparing consumers and regulators to take over the control from drivers.
Primary challenges involved in the advent of autonomous cars are the pricing, safety issues, and consumer understanding. It can be calculated that regulatory guidelines and consumer acceptance can play a vital role in obstructing the development of autonomous cars.
Agile Java application development, combined with tech expertise, can help to create the roadmap for successfully achieving the goal. Technologies that lay grounds for the development of immersive autonomous driving technology includes:
- Machine vision
- Sensor fusion
Each of the above-mentioned components is responsible for handling different domains of software engineering. One of these elements may focus on sensor programming; others might focus on artificial intelligence, while others will handle cameras. All the development team members must distribute the workload as per specialized skills, from developing data layers to building V2X connectivity applications.
Today’s legacy cars have reached level 3 autonomy with innovative software application development in the area of autonomous driving. It will take a couple of years till we see fully automated cars on our roads. Autonomous driving is an exceptional engineering area that requires excellent technical skills and agile software to make it a reality in the near future.
Fully autonomous vehicles are unlikely to rule the market until the regulatory and technological issues are resolved. However, the fact is that, nor the automotive giants or the leading tech players have this outstanding skill set. However, if hardware and software service providers can shake hands and collaborate for developing automated solutions, it is possible that they can meet the business-specific requirements of the automotive industry.