A survey on motion prediction and risk assessment for intelligent vehicles

International audienceWith the objective to improve road safety, the automotive industry is moving toward more “intelligent” vehicles. One of the major challenges is to detect dangerous situations and react accordingly in order to avoid or mitigate accidents. This requires predicting the likely evolution of the current traffic situation, and assessing how dangerous that future situation might be. This paper is a survey of existing methods for motion prediction and risk assessment for intelligent vehicles. The proposed classification is based on the semantics used to define motion and risk. We point out the tradeoff between model completeness and real-time constraints, and the fact that the choice of a risk assessment method is influenced by the selected motion model

Driver-centric Risk Object Identification

A massive number of traffic fatalities are due to driver errors. To reduce fatalities, developing intelligent driving systems assisting drivers to identify potential risks is in urgent need. Risky situations are generally defined based on collision prediction in existing research. However, collisions are only one type of risk in traffic scenarios. We believe a more generic definition is required. In this work, we propose a novel driver-centric definition of risk, i.e., risky objects influence driver behavior. Based on this definition, a new task called risk object identification is introduced. We formulate the task as a cause-effect problem and present a novel two-stage risk object identification framework, taking inspiration from models of situation awareness and causal inference. A driver-centric Risk Object Identification (ROI) dataset is curated to evaluate the proposed system. We demonstrate state-of-the-art risk object identification performance compared with strong baselines on the ROI dataset. In addition, we conduct extensive ablative studies to justify our design choices.Comment: Submitted to TPAM

Driving assistance method and system

A driving assistance system includes a sensor set, a data storage device and an output device. The sensor set observes apparent states of each road user of the plurality of road users at successive time steps, the data processor assigns a behavioral model stored in the data storage device, the data processor calculating a new maneuver distribution that is a probability distribution over a finite plurality of alternative maneuvers and a new state distribution that is a probability distribution of possible states for each alternative maneuver of the finite plurality of alternative maneuvers. The data processor determines a risk of collision of the road vehicle with another road user, based on the new maneuver and state distributions of the target road user, and outputs one or more of a driver warning signal and executes an avoidance action if the risk of collision exceeds a predetermined threshold