The existing rear-end collision warning systems (CWS) that involve the variable perception-reaction time (PRT) have some negative effects on the collision warning performance due to the poor adaptive capability for the influence of different PRTs. To deal with the related problems, several studies have been conducted based on nonparametric approaches. However, the previous nonparametric methods are of doubtful validity with different PRTs. Moreover, there is a lack of consideration for the criterion to split the real-time data into training and testing sets in terms of enhancing the algorithm performance. In this paper, we propose multilayer perceptron neural-network-based rear-end collision warning algorithm (MCWA) to develop a real-time CWS without any influence of human PRTs. Through a sensitivity analysis, the optimal criterion for splitting real-time data into training and prediction is found in terms of a tradeoff between training time and algorithm accuracy. Comparison study demonstrates that the proposed algorithm outperforms other previous algorithms for predicting the potential rear-end collision by detecting severe deceleration in advance.

Surrogate Safety Measure (SSM) is one of the most widely used methods for identifying future threats, such as rear-end collision. Various SSMs have been proposed for the application of Advanced Driver Assistance Systems (ADAS), including Forward Collision Warning System (FCWS) and Emergency Braking System (EBS). The existing SSMs have been mainly used for assessing criticality of a certain traffic situation or detecting critical actions, such as severe braking maneuvers and jerking before an accident. The ADAS shows different warning signals or movements from drivers’ driving behaviours depending on the SSM employed in the system, which may lead to low reliability and low satisfaction. In order to explore the characteristics of existing SSMs in terms of human driving behaviours, this study analyzes collision risks estimated by three different SSMs, including Time-To-Collision (TTC), Stopping Headway Distance (SHD), and Deceleration-based Surrogate Safety Measure (DSSM), based on two different car-following theories, such as action point model and asymmetric driving behaviour model. The results show that the estimated collision risks of the TTC and SHD only partially match the pattern of human driving behaviour. Furthermore, the TTC and SHD overestimate the collision risk in deceleration process, particularly when the subject vehicle is faster than its preceding vehicle. On the other hand, the DSSM shows well-matched results to the pattern of the human driving behaviour. It well represents the collision risk even when the preceding vehicle moves faster than the follower one. Moreover, unlike other SSMs, the DSSM shows a balanced performance to estimate the collision risk in both deceleration and acceleration phase. These research findings suggest that the DSSM has a great potential to enhance the driver’s compliance to the ADAS, since it can reflect how the driver perceives the collision risks according to the driving behaviours in the car-following situation.

One of the most widely used advanced driver assistance systems (ADAS) for preventing pedestrian–vehicle collisions is the intersection collision warning system (ICWS). Most previous ICWSs have been implemented with in-vehicle distance sensors, such as radar and lidar. However, the existing ICWSs show some weaknesses in alerting drivers at intersections because of limited detection range and field-of-view. Furthermore, these ICWSs have difficulties in identifying the pedestrian’s crossing intention because the distance sensors cannot capture pedestrian characteristics such as age, gender, and head orientation. To alleviate these defects, this study proposes a novel framework for vision sensor-based ICWS under a cloud-based communication environment, which is called the intersection pedestrian collision warning system (IPCWS). The IPCWS gives a collision warning to drivers approaching an intersection by predicting the pedestrian’s crossing intention based on various machine learning models. With real traffic data extracted by image processing in the IPCWS, a comparison study is conducted to evaluate the performance of the IPCWS in relation to warning timing. The comparison study demonstrates that the IPCWS shows better performance than conventional ICWSs. This result suggests that the proposed system has a great potential for preventing pedestrian–vehicle collisions by capturing the pedestrian’s crossing intention.

A previously developed real-time forward collision warning system (RCWS) using a multi-layer perceptron neural network (MLPNN) with a single hidden layer aims to be implemented with in-vehicle sensor and smartphone under cloud-based communication environment. However, several issues exist concerning the communication delay between the smartphone and the cloud server, especially when uploading massive traffic information to the cloud server simultaneously. In order to mitigate the impact of the delay, this research proposes two modified RCWSs using an advanced feed-forward neural network (F2N2). One of them involves MLPNN with two hidden layers and the other includes radial basis function network. The modified RCWSs are evaluated by the real-time warning accuracy under different market penetration rates (MPRs) and delays. The evaluation shows that the warning performances of each RCWS increase when the MPR increases or the delay decreases overall. In addition, the modified RCWSs outperform the original one in all conditions. Furthermore, the performance gap between the modified RCWSs increases as the MPR decreases and the delay increases. These findings suggest that the advanced F2N2 model can be an effective alternative for uprating the performance of the RCWS, particularly under a large delay with low MPR.

A rear-end Collision Warning System (CWS) is applied for mitigating collision risk to the frontal motor vehicle under the traffic conditions. Most of the previous studies have been performed to address the braking behavior related problems based on the deterministic or stochastic parametric methods. However, these algorithms are of doubtful validity in the context of individual driving characteristics such as Perception-Reaction Time (PRT). This paper proposes a framework on Rear-end CWS to take into consideration of PRT effects based on the Artificial Neural Network (ANN). Multi-layer perceptron neural network based rear-end collision warning algorithm (MCWA) is developed and evaluated through a comparison between the conventional algorithms such as Time To Collision (TTC) and Stopping Distance Algorithm (SDA). The comparison study demonstrates that the proposed algorithm outperforms other traditional algorithms for detecting and predicting the rear-end collision risks. The proposed algorithm could be used for rear-end collision warning in car-following case without the influence of different human PRT.