{"title":"别惹上麻烦!自动驾驶汽车的风险意识决策","authors":"Kasra Mokhtari, Alan R. Wagner","doi":"10.1109/RO-MAN53752.2022.9900795","DOIUrl":null,"url":null,"abstract":"Risk is traditionally described as the expected likelihood of an undesirable outcome, such as a collision for an autonomous vehicle. Accurately predicting risk or potentially risky situations is critical for the safe operation of an autonomous vehicle. This work combines use of a controller trained to navigate around individuals in a crowd and a risk-based decision-making framework for an autonomous vehicle that integrates high-level risk-based path planning with a reinforcement learning-based low-level control. We evaluated our method using a high-fidelity simulation environment. We show our method results in zero collisions with pedestrians and predicted the least risky path, time to travel, or day to travel in approximately 72% of traversals. This work can improve safety by allowing an autonomous vehicle to one day avoid and react to risky situations.","PeriodicalId":250997,"journal":{"name":"2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Don’t Get into Trouble! Risk-aware Decision-Making for Autonomous Vehicles\",\"authors\":\"Kasra Mokhtari, Alan R. Wagner\",\"doi\":\"10.1109/RO-MAN53752.2022.9900795\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Risk is traditionally described as the expected likelihood of an undesirable outcome, such as a collision for an autonomous vehicle. Accurately predicting risk or potentially risky situations is critical for the safe operation of an autonomous vehicle. This work combines use of a controller trained to navigate around individuals in a crowd and a risk-based decision-making framework for an autonomous vehicle that integrates high-level risk-based path planning with a reinforcement learning-based low-level control. We evaluated our method using a high-fidelity simulation environment. We show our method results in zero collisions with pedestrians and predicted the least risky path, time to travel, or day to travel in approximately 72% of traversals. This work can improve safety by allowing an autonomous vehicle to one day avoid and react to risky situations.\",\"PeriodicalId\":250997,\"journal\":{\"name\":\"2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)\",\"volume\":\"94 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RO-MAN53752.2022.9900795\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RO-MAN53752.2022.9900795","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Don’t Get into Trouble! Risk-aware Decision-Making for Autonomous Vehicles
Risk is traditionally described as the expected likelihood of an undesirable outcome, such as a collision for an autonomous vehicle. Accurately predicting risk or potentially risky situations is critical for the safe operation of an autonomous vehicle. This work combines use of a controller trained to navigate around individuals in a crowd and a risk-based decision-making framework for an autonomous vehicle that integrates high-level risk-based path planning with a reinforcement learning-based low-level control. We evaluated our method using a high-fidelity simulation environment. We show our method results in zero collisions with pedestrians and predicted the least risky path, time to travel, or day to travel in approximately 72% of traversals. This work can improve safety by allowing an autonomous vehicle to one day avoid and react to risky situations.