LEADER 01981nam 2200325 450 001 996581066003316 005 20231213213207.0 010 $a1-5044-9458-X 035 $a(CKB)4100000012903189 035 $a(NjHacI)994100000012903189 035 $a(EXLCZ)994100000012903189 100 $a20231213d2023 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aWhite Paper $eExample Applications of IEEE Std 2846-2022 to Formal Safety-Related Models /$fInstitute of Electrical and Electronics Engineers 210 1$aNew York :$cIEEE,$d[2023] 215 $a1 online resource (26 pages) 330 $aWhile automated driving system (ADS)-operated vehicles hold the potential for safety improvement compared to human drivers, the recognition that transportation will continue to entail some level of risk has to be considered. Human drivers rely on extensive daily experience in their interactions with other agents on the road, which helps them craft assumptions about reasonably foreseeable behavior of other road users. Similarly, ADS-operated vehicles will also need to make assumptions. Such assumptions play a role within ADS safety-related models, which provide a representation of safety-relevant aspects of driving behavior pertaining to both ADS-operated vehicles and other road users. Furthermore, formal safety-related models provide transparency and certainty in ADS decision-making contexts as they can be formally verified. Therefore, this paper introduces how several safety-related models are making use of reasonably foreseeable assumptions to help with the decision making of an ADS-operated vehicle. 606 $aAutomobiles$xSafety measures 615 0$aAutomobiles$xSafety measures. 676 $a629.231 801 0$bNjHacI 801 1$bNjHacl 906 $aBOOK 912 $a996581066003316 996 $aWhite paper$91575265 997 $aUNISA