In today’s digital economy, nothing is prized like data. Actually, it has been referred to as “the new oil” and “resource.” It is highly-priced because it is what powers artificial intelligence (AI) and machine learning (ML), the disruptive technologies of today.
Talking of AI and ML, we can’t fail but discuss the ever-approaching period of self-driving cars. There’s no doubt that we’ve come a long way in automotive technology. From the time when we only had manual transmission vehicles, nowadays, vehicles come with a plethora of sensors that enable you to drive safely.
However, the emergence of self-driving cars is a new chapter because it incorporates artificial intelligence, machine learning, IoT and big data. Clearly, we are in the process of developing self-driving cars, but the troubling question is, what is the role of big data?
While several technologies have been integrated into the development of these cars, it is not in doubt that big data has played an overarching role. In fact, McKinsey projects big data in vehicles to become a $750 billion industry by 2030.
So, let’s look at 5 reasons why big data is the future of self-driving cars.
Big Data and Future of Self-driving Cars
Car manufacturers use big data from real-world driving experiences to understand any gaps in essential parameters, such as battery life and fuel efficiency, that determine overall performance. The insights obtained are then used to design vehicles that are safe and meet customer preferences.
Moreover, using big data and predictive analytics, manufacturers can improve on already existing designs to produce more efficient models . Big data makes the design and manufacturing process more efficient because it relies on facts. For example, at the assembly line, data obtained from observations can be used to improve operational efficiency.
Tesla and Ford have been harnessing big data to optimize road safety and customer experience – eventually laying the foundation for autonomous vehicle (AV) development. In both instances, customers of both automakers are used as test drivers.
For example, Tesla provided its customers with an optimal technology package in 2014, which utilized sensors and cameras to learn about driving behaviors and support collision warning systems. After a year of analyzing the data received, Tesla approved 60,000 cars for self-driving capability. Meaning, the cars could govern braking, steering, and lane changing.
Self-driving cars are proving to be more intelligent in areas where human shortcomings are strikingly glaring. For example, when it comes to blind spots, humans are unable to foresee them, but AVs can. Through the use of sensors, the cars can foresee the danger in areas that humans can’t.
Typically, before an autonomous vehicle safely navigates the road, the engineers have to train the AI algorithms that facilitate the self-drive. In doing so, they rely on data collected from GPS receivers, sensors around the car, and short-range wireless network interfaces.
The sensors gather data, which is used to teach the vehicle of the varying driving scenarios. Big data is vital because it shows the AI how to recognize the dangerous objects on the road and when there is need for mechanical maintenance.
In summary, big data gives AVs the power to “think.” It also equips the vehicles with the requisite technology that enables them to “see” their surrounding environment in ways that surpasses human ability.
Lack of parking, congestion, and long commutes are some of the problems on our roads today. Big data is set to improve the situation through the use of autonomous vehicles. It provides the necessary information that helps these cars to select routes that are less congested and with adequate parking.
Driverless cars generate and consume big data to detect and sense traffic conditions. Actually, they can generate Gigabytes of data per second (GBPS). They then correlate it with ML in the cloud to educate the fleet, while edge computing decides the action an individual car can take at a particular moment and situation.
Already, Google has Google Maps and Waze to help motorists in tracking congestion in a route and offering alternatives. However, big data will definitely take the thing to the next level. The data obtained from radars mounted on the car, cameras, sensors, the cloud, and similar data from other vehicles will guide in adaptive decision making, automatically tailoring routes to maximize the efficiency of fuel used and time spent.
Soon, AVs will be ubiquitous as most cities in the US make long-range transportation plans. According to a report, Autonomous Vehicle Pilots Across America, more than half of US cities are preparing their streets in anticipation of autonomous vehicles.
As the cities plan for the AVs, they too have to rely on big data. The major technological challenges facing smart cities is on how to process the voluminous and geographically scattered sources of data
Despite the challenge, smart cities will be powered by IoT sensors, Mobility as a Service (MaaS) platforms, and big data systems, with the connected car featuring predominantly. So with forecasts indicating that the number of autonomous vehicles is projected to hit 745,705 units by 2023, big data will play a significant role in synchronizing the development of the cities and self-driving cars.
The self-driving vehicle is the archetypical Internet of Things(IoT) device. It not only handles onboard computing tasks but is excellently connected with multiple networks and devices. The system facilitates real-time communication with the surrounding environment, facilitating split-second decisions from the information received.
Apart from the information gathered from the surrounding, such as potential hazards and road conditions, AVs will be able to communicate with each other. Therefore, every car on the road will be an extension of the others’ sensors. Together, the two systems form an interactive routing map that facilitates a seamless navigation on the road. All these will rely on big data to accomplish.