What you’ll learn:
- How AI-driven steering systems enhance vehicle safety and responsiveness.
- What are the most common applications of AI in steering?
- How to overcome key challenges in implementing AI-enhanced steering.
- Real-world examples of successful AI steering systems.
Artificial intelligence (AI) is redefining how automobiles are made, purchased, maintained, and driven by impacting everything from braking to accelerating to steering. Today’s AI-driven steering systems can detect and respond to potential hazards faster than human drivers. By monitoring sensor and camera inputs and analyzing driver behavior, AI can process data and make real-time decisions that minimize risks and optimize steering response.
Currently, the most common applications of AI-enhanced steering in vehicles are autonomous lane-keeping, automatic parking, low-speed maneuvers, predictive steering adjustments, and driver assistance in semi-autonomous vehicles. As the benefits of AI-enhanced steering systems become more widely recognized, the fear people initially felt about self-driving cars is quickly dissipating—and interest is increasing.
In the recent Autonomous Vehicles: Consumer Trends and Attitudes Survey, 73% of drivers between the ages of 21-29 and 69% of drivers between the ages of 30-39 stated “they want fully autonomous driving in their cars in the next five years.” A 2024 University of Michigan Autonomous Vehicles Fact Sheet indicates autonomous vehicles have the potential to reduce crashes by 90%, potentially saving approximately $190 billion per year. To realize that benefit, it’s imperative to overcome specific challenges.
Challenges that AI Steering Systems Must Address
To realize the potential crash reduction and monetary savings offered by AI steering systems, providers need to address four critical challenges:
- Data quality: Maintaining high-quality data is critical since poor-quality data can seriously compromise steering system safety and performance. Effective strategies for ensuring quality data include creating a clear definition of data requirements, using data pre-processing, relying on redundant data sources, implementing real-time data validation, utilizing continuous monitoring and feedback loops, and conducting extensive testing.
- Compliance: Compliance with industry standards and regulations requires a well-thought-out, highly structured plan that includes staying up-to-date on relevant guidelines, developing a standards-compliant development process, and conducting robust risk assessment. In addition, it’s essential to consistently track AI performance; emphasize cybersecurity; implement extensive training; and conduct regular software updates, system audits, and reviews.
- Safety and redundancy: Maintaining a high level of safety and redundancy is crucial for ensuring reliable steering-system operation under all conditions. Ways to boost these critical design elements include implementing functional-safety principles, utilizing redundant architecture, employing multi-sensor fusion, monitoring systems in real-time, conducting expansive AI training, and applying a fail-operational design with rigorous testing and system certification.
- Real-world complexity: Designing a system that can handle unpredictable, dynamic, and diverse operating conditions is critical. This approach requires studying real-world scenarios; using extensive datasets to train AI; equipping the system with real-time learning capabilities; utilizing simulation and virtual testing; continuing learning post-deployment; implementing a modular and scalable design, and more.
It’s encouraging that many companies are already successfully overcoming these critical challenges. One example comes from GM with its hands-free driver-assistance technology called Super Cruise. Super Cruise relies on a system of cameras, sensors, GPS, and LiDAR map data to detect curves and ensure precise vehicle positioning. When drivers are on a compatible road, they can use the system for hands-free vehicle operation. The system’s head-tracking software ensures drivers continue to pay attention to the road so that they can quickly take back control if needed.
Waymo One is another company that’s effectively utilizing AI in steering. Its autonomous ride-hailing service is now available in cities like Los Angeles, San Francisco, Phoenix, and Austin. Customers can relax or get work done while the autonomous vehicle drives them to their location.
Bosch is yet one more company that’s enjoying success in autonomous driving. The company developed sensors that enable automated vehicles to better perceive their surroundings. Its AI-based steering systems utilize that information whereby drivers can safely remove their hands from a vehicle’s steering wheel in certain situations.
Advanced Technology Changes How Cars are Steered
With companies like Bosch, GM, and Waymo leading the way, AI has advanced beyond basic automation. Now, it’s used to create highly intelligent steering systems capable of complex decision-making, predictive maintenance, and real-time data analysis.
For example, Arrow recently developed a breakthrough AI-based steering system featuring the NVIDIA Jetson Orin software platform for autonomous vehicles and robots that tracks driver head orientation, using left or right rotation to steer. The groundbreaking technology offers disabled people who previously couldn’t drive a powerful new way to increase their mobility.
As new trends and technologies—such as sensor fusion, advanced perception systems, level 4 and 5 autonomous-vehicle technologies, edge computing for faster AI processing, and AI for advanced driver-assistance systems (ADAS)—are further developed, drivers will experience more enhanced comfort, control, and safety.
That’s why Jensen Huang, founder, president, and CEO of NVIDIA, recently said, “Self-driving cars surely will make a huge contribution to society. We’ll be able to redesign the urban environment so that parks will replace parking lots. Think of the money we’ll save, the reduction in accidents, and the incredible freedom this will provide people who can’t drive today.”
Benefits of AI-Based Steering Too Big to Ignore
Already, the impact of AI on steering has been significant, unlocking benefits far beyond what’s offered by more traditional systems. For instance, AI excels at predictive maintenance to enhance steering safety by analyzing data collected from sensors embedded in the steering system to detect early signs of potential mechanical issues. It then schedules repairs before those safety-risking mechanical issues arise.
This proactive approach minimizes the risk of sudden steering failures, boosting the security of everyone who rides in the vehicle. AI also improves safety by tirelessly monitoring driving conditions and activating pre-programmed responses, such as adjusting the steering angle and torque, to respond to hazards in real-time.
In addition, AI facilitates quantitative and qualitative analyses of available data gathered from cameras and sensors to improve the reliability of steering soundness. Using the data collected, AI adapts simultaneously to factors like road surface, speed, driver behaviors, and traffic, refining steering precision to offer better control and maneuverability. Steering reliability is also enhanced through AI’s detection of patterns and early indicators of wear or potential malfunctions, so that they can be addressed before performance is impacted.
The bottom line is that AI integration in steering systems results in a safer, more reliable individual driving experience and less hazardous roads. These systems could even reduce traffic congestion and decrease the need for personal cars by enabling fully autonomous ride-sharing services that enable multiple passengers to efficiently share a single vehicle.
For automotive manufacturers to capitalize on the benefits of AI-enabled steering systems in the future, it’s crucial for them to address and overcome known challenges. By responsibly integrating AI into their steering systems, manufacturers can dramatically improve vehicle control under various road and environmental conditions as well as enhance steering performance and dependability in ways that simply aren’t possible with traditional engineering.
