Every month I post links to the most recent research into motorcycle safety — crash data, protective equipment, rider training, road design, all of it. Here’s what caught my eye this month — two of the four pieces are on AI-based rider assistance, a field that has lagged the equivalent work done on cars. Fresh peer-reviewed indexing was light this cycle, so a couple of these reach back a little further than the usual four weeks; I’ve flagged the publication date on each. VLM-based advanced rider assistance system for motorcycle safetyA team from Honda Research Institute and the University of Maryland built a rider assistance system that uses vision-language models to read the road ahead and score hazards pixel by pixel — pothole severity, how slippery a puddle looks, the size and depth of a surface defect — then feeds those risk maps to a planner that recommends throttle and steering inputs to keep the rider clear. Tested in the CARLA simulator, it beat the baseline method on success rate and lowered hazard exposure. A preprint dated 27 May, so not yet peer-reviewed.https://arxiv.org/abs/2605.27948 An interpretable AI pipeline that talks riders through hazardsThis one treats a large language model as a co-pilot. The pipeline reads first-person motorcycle video at one frame per second, uses a multimodal model (Pixtral) to describe the scene and YOLOv8 to pick out vehicles, pedestrians and road hazards, then a small Mistral model turns it into short, imperative safety prompts. It runs light enough for on-device use. The authors, led by Andry Rakotonirainy, aim it at young and newly licensed riders making the move from supervised training to riding alone. Early-stage work, evaluated on public point-of-view datasets. Published 13 February.https://doi.org/10.3390/vehicles8020039 What predicts injury severity on rural undivided roadsSubasish Das and colleagues at Texas State University built machine-learning models on US crash data to rank the factors driving how badly a motorcyclist is hurt on rural undivided roads. Helmet use, the first harmful event in the crash, and crash speed came out as the strongest predictors of severity. Published 1 March in Scientific Reports.https://www.nature.com/articles/s41598-026-40755-5 Single-vehicle motorcycle crashes — and the passenger effectWorking from 5,253 single-motorcycle crashes, Radovan Madlenak and co-authors compared five machine-learning models. The headline finding runs against intuition: carrying a passenger was the single most important predictor of injury outcome, but not in the direction you might guess. Roughly 48% of riders carrying a passenger came away uninjured, against none of the solo riders in the dataset, which the authors attribute to more cautious riding with someone on the back. Single-lane carriageways, the bike overturning, contaminated road surfaces and collisions with fixed objects such as trees all raised the risk. The best model, a recurrent neural network, reached 79.56% classification accuracy. Published 5 February.https://doi.org/10.3390/app16031629 That’s it for this month. If you’ve come across safety research I’ve missed, feel free to email me.