Quantifying the Accuracy of Reporting the Onset of Lateral Movement for a Selection of Bicycle Global Positioning System Computers

Bicycle computers and apps record, at minimum, positional data over time, and these data are commonly used in accident reconstruction to understand the behavior of the bicycle and rider prior to an incident in question. These positional data are obtained using the Global Positioning System (GPS), and while their absolute positional accuracy has been the subject of prior research, their accuracy at detecting and reporting particular movements is less studied. To improve the accident reconstruction industry’s understanding of these devices’ performance, this research aims to statistically quantify the temporal and positional accuracy of these devices reporting the onset of a lateral deviation or lane change. Controlled testing was performed and recorded with several commercially available bicycle GPS computers and apps, which were compared to a RaceLogic VBox 3i ADAS with Real-Time Kinematics (RTK) corrections from a RaceLogic Base Station. Three separate test bouts were performed, with each test bout consisting of 30 or 32 repeats of three different lateral deviation maneuvers. The bicycle GPS computers were individually synchronized to the RaceLogic data by offsetting their time to minimize the mean-square positional error across the entire test bout, which enabled calculation of the 50th percentile and 95th percentile absolute positional errors for each device. A custom script was then used to programmatically detect the start of each lateral deviation, and then, confidence intervals were calculated to estimate the probability of each GPS device reporting the start of the lateral deviation with zero lead or lag, with 1 s of lag, or with 0 or 1 s of lag based on the relative timestamps and positional data. All three of the tested bicycle GPS computers had a probability of at least 0.5 of reporting the onset of sharp lateral movements with zero lead or lag based on the time data, while only two of the devices maintained a similarly high probability for the position-based data. The iPhone 17 Pro had a probability greater than 0.6 of detecting the onset of both gradual and sharp lateral movements with 1 s of lag for both the time-based and position-based data. And across all lateral movement types, all devices had a probability of at least 0.6 of reporting the onset of lateral movement with either 0 or 1 s of lag.