PMID- 27840592
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20240331
IS  - 1524-9050 (Print)
IS  - 1558-0016 (Electronic)
IS  - 1524-9050 (Linking)
VI  - 18
IP  - 3
DP  - 2017 Mar
TI  - Accelerated Evaluation of Automated Vehicles Safety in Lane-Change Scenarios 
      Based on Importance Sampling Techniques.
PG  - 595-607
LID - 10.1109/TITS.2016.2582208 [doi]
AB  - Automated vehicles (AVs) must be thoroughly evaluated before their release and 
      deployment. A widely used evaluation approach is the Naturalistic-Field 
      Operational Test (N-FOT), which tests prototype vehicles directly on the public 
      roads. Due to the low exposure to safety-critical scenarios, N-FOTs are time 
      consuming and expensive to conduct. In this paper, we propose an accelerated 
      evaluation approach for AVs. The results can be used to generate motions of the 
      other primary vehicles to accelerate the verification of AVs in simulations and 
      controlled experiments. Frontal collision due to unsafe cut-ins is the target 
      crash type of this paper. Human-controlled vehicles making unsafe lane changes 
      are modeled as the primary disturbance to AVs based on data collected by the 
      University of Michigan Safety Pilot Model Deployment Program. The cut-in 
      scenarios are generated based on skewed statistics of collected human driver 
      behaviors, which generate risky testing scenarios while preserving the 
      statistical information so that the safety benefits of AVs in nonaccelerated 
      cases can be accurately estimated. The cross-entropy method is used to 
      recursively search for the optimal skewing parameters. The frequencies of the 
      occurrences of conflicts, crashes, and injuries are estimated for a modeled AV, 
      and the achieved accelerated rate is around 2000 to 20 000. In other words, in 
      the accelerated simulations, driving for 1000 miles will expose the AV with 
      challenging scenarios that will take about 2 to 20 million miles of real-world 
      driving to encounter. This technique thus has the potential to greatly reduce the 
      development and validation time for AVs.
FAU - Zhao, Ding
AU  - Zhao D
AD  - University of Michigan Transportation Research Institute, Ann Arbor, MI 481099 
      USA.
FAU - Lam, Henry
AU  - Lam H
AD  - Department of Industrial and Operations Engineering, University of Michigan, Ann 
      Arbor, MI 48109 USA.
FAU - Peng, Huei
AU  - Peng H
AD  - Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109 
      USA.
FAU - Bao, Shan
AU  - Bao S
AD  - University of Michigan Transportation Research Institute, Ann Arbor, MI 481099 
      USA.
FAU - LeBlanc, David J
AU  - LeBlanc DJ
AD  - University of Michigan Transportation Research Institute, Ann Arbor, MI 481099 
      USA.
FAU - Nobukawa, Kazutoshi
AU  - Nobukawa K
AD  - University of Michigan Transportation Research Institute, Ann Arbor, MI 481099 
      USA.
FAU - Pan, Christopher S
AU  - Pan CS
AD  - Division of Safety Research, National Institute for Occupational Safety and 
      Health, Centers for Disease Control and Prevention, Morgantown, WV 26505 USA.
LA  - eng
GR  - CC999999/Intramural CDC HHS/United States
PT  - Journal Article
DEP - 20160805
PL  - United States
TA  - IEEE trans Intell Transp Syst
JT  - IEEE transactions on intelligent transportation systems : a publication of the 
      IEEE Intelligent Transportation Systems Council
JID - 101213161
PMC - PMC5103645
MID - NIHMS822789
OTO - NOTNLM
OT  - Active safety systems
OT  - automated vehicles (AVs)
OT  - autonomous emergency braking (AEB)
OT  - crash avoidance
OT  - importance sampling (IS)
OT  - lane change
EDAT- 2016/11/15 06:00
MHDA- 2016/11/15 06:01
PMCR- 2017/02/05
CRDT- 2016/11/15 06:00
PHST- 2016/11/15 06:00 [entrez]
PHST- 2016/11/15 06:00 [pubmed]
PHST- 2016/11/15 06:01 [medline]
PHST- 2017/02/05 00:00 [pmc-release]
AID - 10.1109/TITS.2016.2582208 [doi]
PST - ppublish
SO  - IEEE trans Intell Transp Syst. 2017 Mar;18(3):595-607. doi: 
      10.1109/TITS.2016.2582208. Epub 2016 Aug 5.