Efficient statistical validation of machine learning systems for autonomous driving

IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD

Volumn 07-10-November-2016, Issue , 2016, Pages

  Shi, Weijing ^a   Alawieh, Mohamed Baker ^a   Li, Xin ^a   Yu, Huafeng ^b   Arechiga, Nikos ^c   Tomatsu, Nobuyuki ^c  

^a CARNEGIE MELLON UNIVERSITY   (United States)

^b BOEING COMPANY   (United States)

^c Toyota InfoTechnology Center   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; AUTOMOBILE DRIVERS; AUTOMOTIVE INDUSTRY; COMPUTER AIDED DESIGN; FAILURE ANALYSIS; LEARNING SYSTEMS; MARKOV PROCESSES; MONTE CARLO METHODS; NUMERICAL METHODS;

AUTONOMOUS DRIVING; ENVIRONMENT PERCEPTIONS; INTELLIGENT DRIVER ASSISTANCES; MARKOV CHAIN MONTE CARLO ALGORITHMS; NUMERICAL EXPERIMENTS; PEDESTRIAN DETECTION; STATISTICAL VALIDATION; VEHICLE STABILIZATION;

LEARNING ALGORITHMS;

EID: 85000885336     PISSN: 10923152     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2966986.2980077     Document Type: Conference Paper

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