Reachability-Based Confidence-Aware Probabilistic Collision Detection in Highway Driving  

作　　者：Xinwei Wang Zirui Li Javier Alonso-Mora Meng Wang 

机构地区：[1]School of Engineering and Materials Science,Queen Mary University of London,London E14NS,UK [2]Department of Cognitive Robotics,Delft University of Technology,Delft 2628 CD,Netherlands [3]School of Mechanical Engineering,Beijing Institute of Technology,Beijing 100081,China [4]Chair of Traffic Process Automation,“Friedrich List”Faculty of Transport and Traffic Sciences,TU Dresden,Dresden 01069,Germany

出　　处：《Engineering》2024年第2期90-107,共18页工程（英文）

基　　金：supported by the proactive SAFEty systems and tools for a constantly UPgrading road environment(SAFE-UP)project;funding from the European Union’s Horizon 2020 Research and Innovation Program(861570)。

摘　　要：Risk assessment is a crucial component of collision warning and avoidance systems for intelligent vehicles.Reachability-based formal approaches have been developed to ensure driving safety to accurately detect potential vehicle collisions.However,they suffer from over-conservatism,potentially resulting in false–positive risk events in complicated real-world applications.In this paper,we combine two reachability analysis techniques,a backward reachable set(BRS)and a stochastic forward reachable set(FRS),and propose an integrated probabilistic collision–detection framework for highway driving.Within this framework,we can first use a BRS to formally check whether a two-vehicle interaction is safe;otherwise,a prediction-based stochastic FRS is employed to estimate the collision probability at each future time step.Thus,the framework can not only identify non-risky events with guaranteed safety but also provide accurate collision risk estimation in safety-critical events.To construct the stochastic FRS,we develop a neural network-based acceleration model for surrounding vehicles and further incorporate a confidence-aware dynamic belief to improve the prediction accuracy.Extensive experiments were conducted to validate the performance of the acceleration prediction model based on naturalistic highway driving data.The efficiency and effectiveness of the framework with infused confidence beliefs were tested in both naturalistic and simulated highway scenarios.The proposed risk assessment framework is promising for real-world applications.

关 键 词：Probabilistic collision detection Confidence awareness Probabilistic acceleration prediction Reachability analysis Risk assessment 

分 类 号：U495[交通运输工程—交通运输规划与管理]