Motorcyclists too often collide with other road users who pull out of side roads in front of them. These other road users typically report making all the necessary visual checks, despite failing to see the approaching motorcycle. These Look But Fail To See errors appear to be attenuated in road users who themselves have motorcycling experience, suggesting that motorcycle exposure may lower thresholds for spotting these vulnerable road users through natural perceptual learning. This raises the possibility that perceptual training could improve car drivers' abilities to spot motorcycles. Two experiments are reported. The first experiment demonstrated that a T-junction task, requiring participants to detect an approaching vehicle in briefly displayed images, was sensitive to participants' motorcycle experience, with dual drivers (who both ride motorcycles and drive cars) performing better than average car drivers. Following this, a second experiment split the car drivers into 2 groups. One group undertook a Pelmanism task requiring participants to match pairs of motorcycles, while the control group had to match pairs of fruit. When the two groups were retested on the T-junction task, the group who had undergone perceptual training for motorcycles via the Pelmanism task, were better able to identify approaching motorcycles, but not approaching cars. The results suggest that gamification of perceptual training for motorcycle detection provides a novel opportunity to improve driver safety.