Intelligent‐driving technologies play crucial roles in reducing road‐traffic accidents and ensuring more convenience while driving. One of the significant challenges in developing an intelligent vehicle is how to operate it safely without causing fear in other human drivers. This paper presents a new behavioral decision‐making model to achieve both safety and high efficiency and also to reduce the adverse effect of autonomous vehicles on the other road users while driving. Moreover, we attempt to adapt the model for human drivers so that users can understand, adapt, and utilize intelligent‐driving technologies. Furthermore, this paper proposes a combined spring model for assessing driving risk. Thus, we analyze some driving characteristics of drivers and choose “safety” and “high efficiency” as the two main factors that are pursued by drivers while driving. Based on the principle of least action, a multiobjective optimization cost function is established for the decision‐making model. Finally, we design six unprotected left‐turn scenarios at a T‐intersection and three unprotected left‐turn scenarios at a standard two‐lane intersection for applying simulation algorithm and provide a decision‐making map for developing intelligent‐driving technologies. Based on the principle of least action, this paper demonstrates that optimization theory can give insight into drivers’ behavior and can also contribute to the development of intelligent‐driving algorithms. The experimental results reveal that the behavioral decision‐making model can always avoid collision accidents on the premise of ensuring certain efficiency, and it can achieve 97.01%, 94.52%, 96.67%, 91.18%, 101.27%, 83.33%, 102.94%, 103.03%, and 105.77% of time to intersection's maximum pass rate in the considered nine scenarios.
Understanding the dynamic characteristics of surrounding vehicles and estimating the potential risk of mixed traffic can help reliable autonomous driving. However, the existing risk assessment methods are challenging to detect dangerous situations in advance and tackle the uncertainty of mixed traffic. In this paper, we propose a probabilistic driving risk assessment framework based on intention identification and risk assessment of surrounding vehicles. Firstly, we set up an intention identification model (IIM) via long short-term memory (LSTM) networks to identify the intention possibility of the surrounding vehicles. Then a risk assessment model (RAM) based on the driving safety field is employed to output the potential risk. Specifically, driving safety field can reflect the coupling relationship of drivers, vehicles, and roads by analyzing their interaction. Finally, an integrated risk evaluation model combining both IIM and RAM is developed to form a dynamic potential risk map considering multi-vehicle interaction. For example, in a typical but challenging lane-changing scenario, an intelligent vehicle can assess its driving status by calculating a risk map in real time that represents the risk generated by the estimated intentions of surrounding vehicles. Furthermore, simulations and naturalistic driving experiments are conducted in the extracted lane-changing scenarios, and the results verify the effectiveness of the proposed model considering lane-changing behavior interaction.
Over the past decades, there has been significant research effort dedicated to the development of intelligent vehicles and V2X systems. This paper proposes a road traffic risk assessment method for road traffic accident prevention of intelligent vehicles. This method is based on HMM (Hidden Markov Model) and is applied to the prediction of steering angle status to (1) evaluate the probabilities of the steering angle in each independent interval and (2) calculate the road traffic risk in different analysis regions. According to the model, the road traffic risk is quantified and presented directly in a visual form by the time-varying risk map, to ensure the accuracy of assessment and prediction. Experiment results are presented, and the results show the effectiveness of the assessment strategies.
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.
Purpose-The purpose of this paper is to accurately capture the risks which are caused by each road user in time. Design/methodology/approach-The authors proposed a novel risk assessment approach based on the multi-sensor fusion algorithm in the real traffic environment. Firstly, they proposed a novel detection-level fusion approach for multi-object perception in dense traffic environment based on evidence theory. This approach integrated four states of track life into a generic fusion framework to improve the performance of multi-object perception. The information of object type, position and velocity was accurately obtained. Then, they conducted several experiments in real dense traffic environment on highways and urban roads, which enabled them to propose a novel road traffic risk modeling approach based on the dynamic analysis of vehicles in a variety of driving scenarios. By analyzing the generation process of traffic risks between vehicles and the road environment, the equivalent forces of vehicle-vehicle and vehicle-road were presented and theoretically calculated. The prediction steering angle and trajectory were considered in the determination of traffic risk influence area. Findings-The results of multi-object perception in the experiments showed that the proposed fusion approach achieved low false and missing tracking, and the road traffic risk was described as a field of equivalent force. The results extend the understanding of the traffic risk, which supported that the traffic risk from the front and back of the vehicle can be perceived in advance. Originality/value-This approach integrated four states of track life into a generic fusion framework to improve the performance of multi-object perception. The information of object type, position and velocity was used to reduce erroneous data association between tracks and detections. Then, the authors conducted several experiments in real dense traffic environment on highways and urban roads, which enabled them to propose a novel road traffic risk modeling approach based on the dynamic analysis of vehicles in a variety of driving scenarios. By analyzing the generation process of traffic risks between vehicles and the road environment, the equivalent forces of vehicle-vehicle and vehicle-road were presented and theoretically calculated.
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