The evolution in autonomy and increase in the adoption of driver assistance systems have generated a need to sense and perceive the surrounding environment of a vehicle entirely and accurately. The fusion of forward-looking sensor data has given OEMs and Tier-I suppliers opportunities to offer multiple ADAS applications at low costs, while providing high redundancy and reliability in perceiving vehicle surroundings.

The use of multiple sensors and sensor data fusion and an increase in the number of ADAS applications have elevated the amount of in-vehicle data exchange to a few gigabytes, which is expected to rise further, as the level of vehicle autonomy goes up. This will generate the need to increase the speed of data transfers within the vehicle communication network and the use of high-powered control and processing units, which will, in turn, increase the complexity of the vehicle E/E architecture.

The evolution of Connected Autonomous Shared and Electric (CASE) in today’s vehicles has urged OEMs to redesign product development ground up. OEMs are developing new vehicle platforms or changing the design of the existing ones, thereby altering the internal wiring and communication protocols to accommodate electric powertrains, connectivity features, and autonomous applications, including the embedded and decision-making software in existing and future vehicles.

The study focuses on the sensor data fusion strategies for ADAS and AD systems in the NA and EU regions, with forecasts running up until 2025. Frost & Sullivan has highlighted the key strategies of sensor data fusion and its influence on the key design elements of E/E architecture and sourcing AD software to achieve L3 and above autonomy. The study discusses the major trends observed in the market and explains the impact scenarios, along with use cases.

  • What is the need for sensor data fusion?
  • What are the different types of sensor data fusion strategies in the market?
  • What is the optimum sensor data fusion strategy for various vehicle segments?
  • What are the key OEM strategies influenced by sensor data fusion to accelerate the development of autonomous driving?
  • What does the autonomous vehicle value chain look like?
  • How are the key sensors in autonomous vehicles evolving and what are their capabilities?
  • How are the sensor requirements changing with the level of autonomy?
  • How is the vehicle E/E architecture evolving and what are the key topologies?
  • What are the business models adopted by the autonomous software developers?