Abstract
Pedestrian–vehicle collision is an important component of traffic accidents. Over the past decades, it has become the focus of academic and industrial research and presents an important challenge. This study proposes a modified Driving Safety Field (DSF) model for pedestrian–vehicle risk assessment at an unsignalized road section, in which predicted positions are considered. A Dynamic Bayesian Network (DBN) model is employed for pedestrian intention inference, and a particle filtering model is conducted to simulate pedestrian motion. Driving data collection was conducted and pedestrian–vehicle scenarios were extracted. The effectiveness of the proposed model was evaluated by Monte Carlo simulations running 1000 times. Results show that the proposed risk assessment approach reduces braking times by 18.73%. Besides this, the average value of TTC−1 (the reciprocal of time-to-collision) and the maximum TTC−1 were decreased by 28.83% and 33.91%, respectively.
Highlights
Pedestrians are a vulnerable group in road traffic accidents
We presented a modified Driving Safety Field (DSF) model for pedestrian–vehicle risk assessment based on pedestrian’s future trajectory predictions, for which multisensors such as LiDAR and monocular cameras were used to collect scenario data in a real traffic environment
The predicted trajectory uncertainty was defined in our modified DSF model for proactive risk assessment by using an attenuation coefficient that was based on the Bellman equation
Summary
Pedestrians are a vulnerable group in road traffic accidents. The Annual Statistical Report for Road Traffic Accidents in China showed that pedestrians accounted for 27.11% of the total number of traffic fatalities and 16.72% of the injured in 2017 [1]. Potential field-based methods for road traffic risk assessments, which have been widely used in obstacle avoidance and path planning for intelligent vehicles in recent years, are able to overcome the aforementioned problems because of their advantages in terms of environmental description. Rasekhipour et al [23] applied the artificial potential field method to path planning, obstacle avoidance, and road traffic risk assessment in intelligent vehicles. Zheng et al [24] developed a model based on equivalent force, which calculated the road traffic risk field in different directions. The potential field-based method for vehicle risk assessment relies on sensor detection information and causes unnecessary or lacking braking occasionally.
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