Potential risk assessment for safe driving of autonomous vehicles under occluded vision

This study aimed to explore how autonomous vehicles can predict potential risks and efficiently pass through the dangerous interaction areas in the face of occluded scenes or limited visual scope. First, a Dynamic Bayesian Network based model for real-time assessment of potential risks is proposed, which enables autonomous vehicles to observe the surrounding risk factors, and infer and quantify the potential risks at the visually occluded areas. The risk distance coefficient is established to integrate the perception interaction ability of traffic participants into the model. Second, the predicted potential risk is applied to vehicle motion planning. The vehicle movement is improved by adjusting the speed and heading angle control. Finally, a dynamic simulation platform is built to verify the proposed model in two specific scenarios of view occlusion. The model has been compared with the existing methods, the autonomous vehicles can accurately assess the potential danger of the occluded areas in real-time and can safely, comfortably, and effectively pass through the dangerous interaction areas.

Limited Visibility Aware Motion Planning for Autonomous Valet Parking Using Reachable Set Estimation

Autonomous driving helps drivers avoid paying attention to keeping to a lane or keeping a distance from the vehicle ahead. However, the autonomous driving is limited by the need to park upon the completion of driving. In this sense, automated valet parking (AVP) system is one of the promising technologies for enabling drivers to free themselves from the burden of parking. Nevertheless, the driver must continuously monitor the automated system in the current automation level. The main reason for monitoring the automation system is due to the limited sensor range and occlusions. For safety reasons, the current field of view must be taken into account, as well as to ensure comfort and to avoid unexpected and harsh reactions. Unfortunately, due to parked vehicles and structures, the field of view in a parking lot is not sufficient for considering new obstacles coming out of occluded areas. To solve this problem, we propose a method that estimates the risks for unobservable obstacles by considering worst-case assumptions. With this method, we can ensure to not act overcautiously while moving safe. As a result, the proposed method can be a proactive approach to consider the limited visibility encountered in a parking lot. In the proposed method, occlusion can be efficiently reflected in the planning process. The potential of the proposed method is evaluated in a variety of simulations.

Personalized Subjective Driving Risk: Analysis and Prediction

Subjective risk assessment is an important technology for enhancing driving safety, because an individual adjusts his/her driving behavior according to his/her own subjective perception of risk. This study presents a novel framework for modeling personalized subjective driving risk during expressway lane changes. The objectives of this study are twofold: (i) to use ego vehicle driving signals and surrounding vehicle locations in a data-driven and explainable approach to identify the possible influential factors of subjective risk while driving and (ii) to predict the specific individual’s subjective risk level just before a lane change. We propose the personalized subjective driving risk model, a combined framework that uses a random forest-based method optimized by genetic algorithms to analyze the influential risk factors, and uses a bidirectional long short term memory to predict subjective risk. The results demonstrate that our framework can extract individual differences of subjective risk factors, and that the identification of individualized risk factors leads to better modeling of personalized subjective driving risk.