Anup Patil is the Chief Executive Officer (CEO) at Intangles, a leading solution provider that leverages its proprietary Digital Twin technology to provide real-time and predictive insights for the mobility industry. A visionary and a first-generation entrepreneur, Mr. Anup Patil spearheads the company’s Strategy, Finance, and People. He also nurtures Intangles’ growth by mapping its vision and aligning it with resources, performance, and accountability across the board.
Artificial Intelligence is a revolution that has swept the globe. The automobile industry has massively benefited from it. AI and ML have deep roots in the Mobility Ecosystem, from Self-Driving Cars to Electric Vehicles. Let’s look at the latest disruptive trends and how they are revolutionising the automotive sector.
Electric Vehicles (EVs)
The EV industry is the most powerful, with AI and Machine Learning taking centre stage in numerous arenas in the automotive sector. However, unlike conventional IC engines, the EV sector is still in its early phases.
Electric Vehicles have created quite a commotion in the market, with sustainability and efficiency taking centre stage. In addition, automakers are constantly stepping up their efforts to provide a varied range of AI-driven features. For example, Machine and Deep Learning Algorithms are being utilised to develop complex driver monitoring systems that examine driving behaviour, cognitive behavioural functions like detecting weariness, alcohol-induced inebriation and security features like biometric verification.
Another critical feature constantly being worked on in EVs is range prediction. Atmospheric elements like temperature and humidity and driving behaviour parameters such as acceleration, braking, and average speeds impact battery management systems. Automotive researchers are performing extensive studies and building complex algorithms that examine all of these circumstances to improve the accuracy of range prediction models.
The availability of charging infrastructure is critical to the widespread adoption of EVs. As a result, efforts are being made to build systems that use geographic data to discover local charging stations, considering fast or slow charging facilities based on the vehicle’s demands.
Vehicle Health Prognostics and Diagnostics
Artificial Intelligence is used in advanced health monitoring systems to provide an accurate remote diagnosis. Digital Twins of drivetrain components are being used to generate predictive insights for use cases like overheating, battery charging, and fuel-air charge composition; furthermore, with minimal user interaction, complete information on fuel efficiency-pilferage, driving performance, route plans, and emissions control system performance. Using AI to identify potential malfunctions minimises downtime and reduces the chance of mission-critical failures. This results in higher toplines and lower maintenance and repair costs.
Autonomous Vehicles (AVs)
We have accomplished building self-driving vehicles, taking us one step closer to our dream of flying automobiles. Cognitive Intelligent Systems use Neural Networks to provide unique features like blind-spot monitoring, traffic analysis, and driver-assist steering in order to minimise accidents and comply with safety regulations. In addition, other AI-enhanced functions, such as adaptive cruise control and automatic parking, increase driving convenience.
The AI bug has also infected the realm of Motorsports. Artificial Intelligence has impacted auto racing in a variety of ways. Surrogate modelling to improve simulation turnarounds, reworking obsolete engineering solutions, and segmenting the fan base for increased advertising are a few examples of the boon that is Artificial Intelligence. Another use of AI in racecars is to improve safety by analysing enormous amounts of historical and real-time data.
Demand will drive the future, and incorporating Artificial Intelligence into Supply Chain Management and Manufacturing will result in better and quicker manufacturing techniques. Designers and product developers may establish well-received models by creating roadmaps based on acquired data about usage and preferences. Robotic assembly lines can accelerate cost savings and productivity by reducing damage and accidents.
Aside from the technological benefits, AI can favourably affect financial metrics through customer profiling. Modern automotive recommendation systems provide a unified client experience for buying and auctioning. With the emergence of several vehicle recommendation platforms in recent years, the sales statistics achieved through digital clients have been astounding.
Artificial Intelligence and Machine Learning are transforming our lives. In the case of the automobile sector, these technologies are essential in reducing and resolving several difficulties, particularly in terms of EV uptake and the development of environmentally friendly automotive technology. The world of AI and ML is full of limitless potential, and it will usher in an era of colossal digital transformation.