Quantifying the Potential: Exploring Gains from ADAS to LiDAR in Autonomous Driving Opportunities

Autonomous driving (AD) has the potential to transform the way consumers experience mobility, offering enhanced safety, convenience, and enjoyment. Time previously spent behind the wheel could be redirected towards activities like video calls with friends, watching movies, or work. Commuters may see increased productivity and shorter workdays with autonomous vehicles (AVs), as they can perform tasks during the commute. This could also enable employees to live farther from the office, potentially attracting more people to rural areas and suburbs. Additionally, AD could improve mobility options for elderly drivers, expanding beyond public transportation or car-sharing services. Safety gains are also anticipated, with a study projecting a 15 percent reduction in accidents by 2030 through advanced driver-assistance systems (ADAS) adoption in Europe.

In addition to these consumer benefits, AD holds the potential to create added value for the automotive industry. Consumer demand for AD features is evident, as per a McKinsey survey, suggesting potential for significant revenue generation.

To fully realize the potential of autonomous driving, auto original equipment manufacturers (OEMs) and suppliers may need to formulate new sales and business strategies, acquire advanced technological capabilities, and address safety concerns.

In this ever-evolving landscape, the ability to make informed decisions amid uncertainty emerges as a critical business capability. Those who can adeptly navigate and manage uncertainty may gain a significant advantage over their competitors.

Adding to this discussion, it's worth noting that Bosch, a prominent German Tier 1 supplier, recently made headlines by announcing its decision to discontinue investment in LiDAR technology. This development opens up an opportunity for other players in the automotive supply chain to explore and invest in LiDAR technologies, which Bosch Mobility has decided to relinquish.

Step 1 - Forecasting Global Vehicles Sales

In the first step of our forecasting process, we leverage historical global vehicle sales data and consider macroeconomic conditions. This data is provided to our DeepFeatTimeGPT model, which is a generative AI forecasting model trained on a vast dataset of billions of data points. Our objective is to forecast the global demand for vehicles over the next 20 years, taking into account the inherent uncertainty of future scenarios. Our goal is to provide insights into the most likely outcome, encompassing an estimate of close to 3 million monthly vehicle sales.

This comprehensive forecasting approach combines the power of AI, extensive historical data, and macroeconomic insights to generate forecasts that can assist in strategic decision-making for the automotive industry and related sectors.

Step 2 - Forecasting the Demand for ADAS Vehicles

Building upon our initial vehicle sales forecast, we now turn our attention to forecasting the demand for Advanced Driver Assistance Systems (ADAS) equipped vehicles. This step involves considering the gradual global diffusion of ADAS technology, specifically targeting vehicles at Level 2 or higher on the automation scale.

To achieve this, we employ a simulation approach that generates millions of scenarios to model the evolution of ADAS adoption over time. This simulation takes into account various factors such as technological advancements, regulatory changes, consumer preferences, and market dynamics. By doing so, we aim to provide a comprehensive understanding of how the demand for ADAS-equipped vehicles is likely to evolve in response to these complex and dynamic variables.

Step 3 - Forecasting Revenue Opportunity for LiDAR

In the third step of our forecasting process, we shift our focus to forecasting the revenue opportunity for LiDAR (Light Detection and Ranging) technology. Specifically, we aim to understand how LiDAR technology will diffuse within the ADAS segment. To achieve this, we employ a simulation approach that considers multiple scenarios for the evolution of LiDAR adoption over time. We incorporate insights from industry experts to ensure our forecasts align with the most accurate and up-to-date information available.

Our simulation model takes into account several critical factors:

1. LiDAR Diffusion Rates: We simulate how quickly LiDAR technology is adopted within the ADAS market. This involves considering factors like technology advancements, regulatory requirements, and market demand.
2. Willingness to Pay by OEMs: We analyze the willingness of Original Equipment Manufacturers (OEMs) to invest in and pay for LiDAR components. This is based on market trends, expert knowledge, and economic considerations.

By considering these factors, we can create millions of scenarios that provide a comprehensive view of the revenue opportunity associated with LiDAR technology. This forecasting step is instrumental in helping stakeholders, including LiDAR manufacturers and automotive OEMs, make informed decisions regarding investments, partnerships, and market strategies related to LiDAR technology in the context of ADAS-equipped vehicles.

Based on our forecast, the revenue opportunity for LiDAR sensors in the automotive industry is estimated to range from approximately $7 billion to $18 billion annually.

Step 4 - Simulating Cash Flow Opportunities for LiDAR R&D

The pivotal question for decision-makers is whether it is worthwhile to invest in LiDAR technology today. To address this critical inquiry, we employ our DeepFeatSimulate model, a sophisticated tool trained on a vast dataset of billions of data points. This model conducts simulations that encompass various scenarios, including required Research and Development (R&D) investments, capital expenditures (CapEx), time to market, and other relevant factors.

By leveraging these simulations, we empower decision-makers with valuable information to assess the feasibility and potential returns on investment associated with LiDAR technology. This simulation is best done by taking into account your internal financial data and historic R&D project data to offer custom recommendations.

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