How does Rods generate pace notes automatically?

Rods analyses road geometry from map data in real time using GPS, calculating corner angles, radii, and sequences to generate audio pace notes without any manual input from the driver.

Do automatic pace notes work without an internet connection?

Yes. Rods works offline using GPS and pre-processed road data, so pace notes continue even in areas with no mobile signal.

How accurate are automatic pace notes compared to rally co-driver notes?

Automatic pace notes reliably detect corner severity and sequence. They lack the nuance a human co-driver adds for surface changes or camber, but they cover the core information most road drivers need.

What is a corner severity scale in pace notes?

A corner severity scale rates how tight a corner is. The standard rally scale runs from 1 (tightest hairpin) to 6 (gentle curve), giving drivers advance warning of how much they need to slow down.

How Rods Generates Pace Notes Automatically

In professional rally, pace notes require days of preparation. A driver and co-driver must physically drive every kilometer of road, manually recording each corner, crest, and hazard in a specialized notebook. This process makes pace notes incredibly valuable — and historically, completely inaccessible to everyday drivers.

Rods changes that equation. The app generates pace notes automatically for any road, delivering real-time audio corner calls without any pre-recording, manual input, or preparation. You start driving, and the app starts calling corners.

This article explains how that technology works — from raw map data to the voice in your speaker.

The Problem: Blind Corners on Unfamiliar Roads

Every winding road presents the same fundamental challenge: you cannot see through corners. When you approach a blind curve on an unfamiliar road, you don't know if the road curves gently, if it tightens into a hairpin, if there's a crest hiding the exit, or if the surface changes mid-corner.

This information gap is where accidents happen. Government road safety data consistently shows that single-vehicle accidents on rural roads are disproportionately caused by drivers entering corners at inappropriate speed — not because they were driving recklessly, but because they lacked information about what the corner actually does.

Rally drivers solved this problem decades ago with pace notes. The concept is proven: advance knowledge of corners reduces surprises, enables better speed management, and makes winding roads both safer and more enjoyable. The challenge was making this concept accessible without the rally infrastructure.

How Rods Analyzes Road Geometry

The foundation of automatic pace note generation is road geometry analysis. Roads are not random squiggles — they follow mathematical curves defined by their engineering, the terrain they traverse, and the constraints of the landscape.

Rods processes road geometry in several stages:

Road Data Acquisition

The app works with road geometry data that describes the shape of roads as sequences of GPS coordinates. Think of it as a very detailed connect-the-dots outline of every road. This data captures the horizontal geometry of the road — how it curves left and right — as well as metadata about the road type, surface, and characteristics.

Corner Detection

From the raw coordinate data, the system identifies corners — sections where the road changes direction significantly. This involves calculating the curvature at each point along the road: how sharply the road bends at that location.

Corner detection isn't simply finding curves. The system must distinguish between actual corners (where a driver needs to change speed or steering input) and gentle road wander (where the road meanders slightly but doesn't require any action). Thresholds for corner detection are calibrated against real driving behavior — the corners Rods identifies are the same corners a human co-driver would note.

Severity Classification

Once corners are identified, each one receives a severity rating on the 1-6 scale. This classification considers several factors:

Radius of curvature — How tight is the bend? Tighter radius = lower number = more severe corner.
Length of corner — How long does the curve persist? A long corner at the same radius feels different from a short one.
Change in direction — How many degrees does the road change heading? A 90-degree turn is fundamentally different from a 30-degree sweep.
Approach geometry — What comes before the corner? A tight turn after a long straight is more surprising than one in a sequence of similar corners.

The classification system is calibrated to match how professional rally co-drivers would rate the same corners. A corner rated "3" by Rods should feel like a 3 to a driver familiar with the rally scale.

Shape Analysis

Beyond basic severity, Rods analyzes how corners change through their length:

Tightening corners — Where the radius decreases through the turn. These are identified by comparing curvature at the entry, middle, and exit of each corner. Tightening corners receive a specific modifier because they're a common accident cause.
Opening corners — Where the radius increases. The driver can accelerate earlier than a constant-radius corner would suggest.
Compound corners — Where multiple curves link together without a straight between them.

Hazard and Surface Data Layers

Corner geometry is the core of pace notes, but Rods adds additional data layers to provide a more complete picture:

Crest Detection

Using elevation data, Rods identifies crests — points where the road rises and then falls. Crests are significant because they reduce visibility (you can't see beyond the top) and unsettle the vehicle (tires lose contact as the road drops away). A corner over a crest is substantially more challenging than the same corner on flat ground.

Road Context

The system considers the broader context of each road: urban vs rural, motorway vs single-track, major route vs minor road. This context influences how calls are prioritized and delivered.

From Data to Voice: Real-Time Callouts

The technical analysis produces a structured dataset: a sequence of corners with severity ratings, directions, modifiers, and distances between them. Transforming this into a useful driving aid requires solving several real-time problems:

GPS Positioning

Rods uses your phone's GPS to determine your position on the road in real time. This position is matched to the analyzed road geometry, so the system knows exactly where you are and what corners are coming up.

Timing and Delivery

Corner calls must arrive at the right time — early enough for the driver to react, but not so early that the information is forgotten. Rods calculates delivery timing based on your current speed and the distance to the next corner. At higher speeds, calls come earlier. At lower speeds, they come later. This dynamic timing mimics how a professional co-driver adjusts their delivery.

Audio Generation

The pace note data is converted into audio calls using voice synthesis optimized for clarity and rhythm. The calls follow the standard pace note format: direction, severity, modifiers, distance. Each element is spoken with the cadence and clarity that rally co-drivers have refined over decades.

The result is a continuous stream of audio information that sounds like a co-driver reading notes — because it's structured exactly the same way.

Offline Mode: GPS-Only Driving

One of the practical challenges of any phone-based driving tool is cellular connectivity. Mountain roads, rural areas, and remote regions often have poor or no cell coverage — exactly the places where road awareness matters most.

Rods addresses this with a GPS-only mode. Road geometry data for your route is loaded before you drive, and the system then operates using only GPS positioning — no cellular connection required. The pace notes are generated from the pre-loaded data and delivered based on your GPS location.

This means Rods works on remote mountain passes, in deep valleys, and in areas with no cell towers. As long as GPS satellites are visible (which they are almost everywhere outdoors), the system functions.

What's Next

Automatic pace note generation is a foundation technology. The core concept — turning road geometry data into driver-relevant audio information — opens several future directions:

Community-enhanced data — Driver feedback can improve corner ratings and add hazard information that map data alone doesn't capture.
Adaptive callouts — Adjusting the level of detail based on driving patterns. A driver on a familiar commute needs less information than one exploring an unknown mountain pass.
Integration with vehicle systems — As cars become more connected, pace note information could integrate with heads-up displays, adaptive cruise control, and other driver assistance systems.

The vision is straightforward: every driver should have access to the road awareness information that rally teams have relied on for decades. Technology makes that possible — not by replacing the driver's judgment, but by ensuring they always know what the road does next.