Navigating urban roads with safety-focused, human-like automated driving experiences

From crowded intersections full of vehicles, two-wheelers and pedestrians to highly congested arterial roadways, dense urban traffic can be complex and often dangerous for road users. Approximately 1.19 million people died in traffic crashes in 2023.¹ In the U.S., 59% of these road fatalities occurred in urban areas - 73% were at intersections.²

Crash avoidance technologies such as advanced driver assistance systems (ADAS) can reduce road incidents, helping to save lives in these complicated scenarios. For example, automatic emergency braking has shown to reduce front-to-rear crashes by 50% and pedestrian crashes by 27%.³

Achieving these results across cities, countries and driving styles is no small task. Traditional, rule-based planning methods for controlling ADAS functionality often struggle to negotiate and adapt to real-time sensor data in dense urban driving scenarios. These human-defined, logic-based planners rely on pre-specified rules, which can't scale to include enough potential scenarios for the planner to react appropriately in any given traffic situation.

AI planner

Introducing an AI-based planner into the system can help to handle the massive amount of input coming into a vehicle as it travels through highly variable and dynamic urban environments. Capable of running large language models while simultaneously processing input from multiple perception systems, an AI planner uses a data-driven approach to learn and adapt in real-time.

Because it is a decision-based transformer, an AI planner understands what information is contextually relevant to the scenario so the driver assistance system can act upon it quickly and effectively. This ability to quickly and holistically process data allows the planner to solve complex urban traffic problems and achieve a more accurate and human-like driving experience.

Best of both with Snapdragon Ride

To provide a human-like experience, the Snapdragon Ride platform employs a hybrid architecture that blends both types of planning. The AI planner is a fully data-driven, transformer-based model, while the traditional planner serves as a safety guardrail and verifier. The models co-exist on the same heterogeneous system-on-a-chip (SoC), running on separate blocks so there is no computational interference. The AI planner benefits from AI acceleration in the neural processing unit (NPU) while traditional planners run on the central processing unit (CPU).

Validated in both simulations and real-world scenarios, the AI planner has demonstrated its ability to solve complex traffic scenarios, including unprotected turns, navigating roundabouts and handling dense traffic merges.

See Snapdragon Ride in action:

Snapdragon Ride: Automated Hands-Free Driving

Aug 19, 2025 | 0:45