Anushruti Singh
Artificial intelligence (AI) is changing the Indian automotive industry for the better. Its stakeholders are working on advancements in urban transportation, autonomous vehicles, intelligent traffic solutions, and even personalisation in travelling. These developments bolster safety, sustainability and efficiency. Consequently, technology solution providers in India are collaborating with auto OEMs to drive AI-powered Mobility-as-a-Service platforms.
SME Futures talked about the various applications of AI and how it is changing the future of transportation with Vineet Singh, Co-Founder of Gauss Moto which has developed multiple solutions to help auto OEMs and transportation companies in accelerating innovation, product development, opening new revenue models and reducing the cost of R&D.
Edited Excerpts:
What opportunities does AI open up for mobility, and how can OEMs leverage them in the short and long run?
AI has the potential to revolutionise the automotive industry by enabling new forms of mobility that are safer, more efficient and more convenient. In the short term, OEMs (original equipment manufacturers) can capitalise on these opportunities by investing in the research and development of AI technologies such as autonomous driving systems, advanced driver assistance systems (ADAS), and connected vehicle technology.
However, on a broader level, OEMs will need to develop a deeper understanding of their customers’ needs and preferences to create personalised experiences that meet those needs. This may involve partnering with other companies or start-ups specialising in data analytics, user experience design, and machine learning. By leveraging these capabilities, OEMs can build innovative products and services that will differentiate them from their competitors and provide value to customers.
How is AI transforming the auto business?
AI is transforming the automobile sector on various fronts. The development of self-driving cars is one of the most significant effects of AI on the auto industry.
Autonomous vehicles have become increasingly capable of navigating roadways and making decisions based on real-time sensor data thanks to the developments in sensors, cameras, LiDAR, and deep learning algorithms. Many major automakers and technology firms are working on building completely autonomous vehicles, which might reduce accidents caused by human error and improve road safety.
Another way in which AI is being impactful is via the Advanced Driver Assistance Systems (ADAS) features such as adaptive cruise control, lane departure warning, blind spot monitoring, and automatic emergency braking. These features use cameras, radar, and LiDAR sensors to monitor the environment around the vehicle and make adjustments accordingly. They also rely on machine learning algorithms to improve over time and better understand driver behaviour.
AI is also being used to optimise vehicle performance and minimise downtime through predictive maintenance. By analysing the large amounts of data that has been collected from the various sensors throughout the vehicle, AI algorithms can identify patterns and anomalies that might indicate future issues before they occur. This allows for proactive repairs and reduces unexpected breakdowns, improving overall fleet efficiency.
This technology is allowing carmakers to offer greater levels of customisation and personalisation to drivers. By collecting and analysing data on individual driving habits, carmakers can tailor the driving experience to each person’s unique preferences. For example, some luxury brands already offer bespoke seat settings, ambient lighting, and infotainment configurations based on facial recognition and voice commands.
Can you highlight the major trends in how automotive players are investing in AI models? What areas are those in?
Automotive players are focusing on investing in AI models across multiple domains, including the areas listed below.
Autonomous Vehicles: Carmakers and suppliers are investing heavily in developing autonomous vehicles, which require sophisticated AI algorithms to process vast amounts of sensor data and make split-second decisions. The goal is to achieve Level 5 autonomy, meaning no human intervention is required.
Electrification: As the shift towards electric vehicles accelerates, automotive players are exploring AI solutions to optimise battery life, charging times, and range. Machine learning algorithms analyse usage patterns, weather conditions, and route information to maximise energy efficiency and extend the lifespan of batteries.
Cybersecurity: With increased connectivity come heightened cybersecurity risks. Automotive players are investing in AI-powered security measures to detect and prevent hacking attempts, protect sensitive data transmitted between vehicles and infrastructure, and ensure secure communication between the different components within a vehicle.
Customer Experience: Automotive players are also utilising AI to enhance customer satisfaction and loyalty by personalising their interactions with them through chatbots, virtual assistants, and voice controls. Additionally, the technology is helping to analyse consumer insights and provide feedback to improve product development and marketing strategies.
Supply Chain Optimisation: Automotive players are using AI to streamline supply chain operations, reducing costs and lead times while ensuring that quality standards are met. Machine learning algorithms analyse production data to identify bottlenecks, optimize inventory management, and forecast demand accurately.
Fleet Management: Companies operating commercial fleets are incorporating AI into their operations to optimise routes, scheduling, and asset tracking. Machine learning algorithms analyse historical data to suggest improvements, reduce fuel consumption, and minimize downtime due to maintenance or traffic congestion.
Product Development: Automotive players are leveraging AI to accelerate product development cycles and bring innovations to market faster. Using generative AI techniques, engineers can quickly explore design options, test simulations, and validate designs against real-world scenarios.
Sales & Marketing: AI is also assisting automotive players to gain insights into buyer behaviour, segment markets properly, and target advertising campaigns efficiently. Meanwhile, machine learning algorithms analyse sales data, website interactions, social media activity, and search queries to help shape brand positioning, pricing strategies, and promotional offers.
With India’s focus on EVs, how are automotive tech companies like yours gaining traction?
There has been a significant push for electrical vehicles (EVs) in India recently, primarily driven by government initiatives like the FAME II scheme. In this context, the Indian start-up ecosystem has seen a surge in new ventures aiming to provide technological solutions for the growing EV sector.
Companies like ours are getting traction from automotive companies who can partner with small to mid-size OEMs and build technology solutions for the following:
EV Mobility Platform: Develop all-electric powertrains with self-driving capabilities, disrupting traditional combustion engines and paving the way for sustainable mobility.
Smart Fleet Management Platform: Create a cloud-based platform that connects various aspects of fleet operations, including routing optimisation, real-time tracking, fuel consumption analysis, and preventive maintenance scheduling.
Autonomous Delivery Bot: Develop a small and agile robot explicitly designed for local deliveries in dense urban areas, easily navigating sidewalks and obstacles.
Battery Swapping Station Network: Establish a network of stations offering fast and convenient replacement of depleted EV batteries with fresh ones, promoting longer travel ranges and reduced charging wait times.
Connected Roadside Assistance Service: Provide a subscription-based emergency assistance programme linking stranded motorists with nearby mechanics, parts suppliers, and recovery teams.
Artificial Intelligence Energy Management Software: Create AI algorithms that optimise battery life and range by analysing driving patterns, weather forecasts, and route information to suggest the most efficient usage strategies.
What could be the challenges in this arena?
There are several potential challenges for the automotive industry while implementing AI. While technology presents opportunities for optimisation and efficiency across different aspects of the business, its adoption requires careful consideration of technical, operational, and cultural issues. A few common challenges associated with integrating AI into the automotive industry are:
Data Quality and Availability: Ensuring the availability and integrity of large amounts of relevant data is crucial for training machine learning models and generating meaningful insights. Poor quality or limited datasets may result in subpar predictions and decisions.
Talent Acquisition and Retention: Attracting and retaining skilled professionals who understand AI concepts and possess programming skills remains challenging. Companies must invest in employee education programmes and competitive compensation packages to attract top talent.
Integration Complexity: Connecting disparate systems and legacy IT architectures while ensuring seamless communication between various departments can pose difficulties during implementation.
Cybersecurity Risks: As more connected systems and sensitive data become part of the equation, cybersecurity threats increase significantly. Robust security measures must be implemented to protect against unauthorised access, malicious attacks, and accidental breaches.
Regulatory Uncertainty: Navigating evolving regulatory frameworks around data protection, safety standards, and ethical considerations adds complexity to AI deployment within the automotive industry.
Cultural Resistance: Change management becomes essential when introducing novel technologies into established workflows and processes. Overcoming scepticism and encouraging employee collaboration requires strong leadership commitment and effective communication strategies.
