Evaluation of emergency driving behaviour and vehicle collision risk in connected vehicle environment: A deep learning approach

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Evaluation of emergency driving behaviour and vehicle collision risk in connected vehicle environment: A deep learning approach

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Peng, Liqun / Wang, Yiting / Zhang, Fan / Zhang, Jisheng / Li, Zhixiong

In the latest connected vehicle (CV) message standards, including SAE J2735‐2016 and T‐CASE 53–2017, the basic safety messages (BSMs) are designed specifically as effective measures for traffic safety management and applications. In this study, a testbed on the Nanchang‐Jiujiang Intelligent Highway in Jiangxi, China is illustrated as an example, and the basic architecture and key technologies is introduced for a proactive traffic safety utilisation, where the core basic safety message (BSM) data are sorted and implemented to perceive and predict risky driving behaviours in a field environment. On this basis, an accurate insight into time‐critical driving safety issues can be achieved by investigating raw BSM data, such as the inter‐vehicle distance, driver manipulation, vehicle speed, and acceleration/deceleration. Furthermore, to effectively take advantage of connected vehicle information and perceive the high uncertainty of driving behaviours during an emergency situation and evaluate the driving safety in mixed traffic scenarios, a long short‐term memory (LSTM) based deep learning framework is introduced to build a multi‐horizon vehicle crash risk prediction model using continuous BSMs as the inputs. The experimental results demonstrate the significance of connected vehicle data and deep learning algorithms for improving driving safety and promoting widespread deployment and application of connected vehicles.