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Connected and Automated Vehicles

Researchers in all groups at UMTRI are performing research to facilitate the development and integration of connected and automated vehicles and systems onto roadways around the world.

Engineering Systems Group
Researchers in UMTRI’s engineering systems group have been working in the area of active safety and automated control technologies for more than 25 years. This group investigates safety and mobility effects of various automated and connected technologies as well as traffic control using existing and future forms of roadway infrastructure. Methods include conducting lab simulations, modeling big data from the field, performing large field studies using instrumented fleets of cars, trucks, buses, and bicycles. They also address cybersecurity issues.

Human Factors Group
A safe and successful deployment of CAVs will depend on how the driver will interact with advanced driving systems. Researchers at UMTRI are developing and testing a host of experimental protocols designed to reveal the potential for inattentive driving, issues of trust, comprehension of how the systems operate, acceptance to how AVs maneuver, as well as developing efficient instructional methods to improve drivers' interaction with CAVs. Researchers also are looking at how CAVs can address some of the most critical issues in transportation safety including the safety of vulnerable road users like pedestrians and bicyclists, as well as how ride sharing might improve accessibility and overall quality of life.

Driver Interface Group
Researchers in the Driver Interface Group at UMTRI are working directly with industry to evaluate the design and usability of vehicle controls, displays and other CAV information systems, and to write design guidelines and develop methods for evaluating future systems. They also develop and test driver interfaces for SAE Level 2 and 3 vehicles.

Biosciences Group
Even with advanced driving systems, crashes will still occur. Occupants in automated vehicles will still need crash protection systems because they can be struck by other vehicles. UMTRI’s experts in the Biosciences Group use physical testing and computational modeling to understand how differences in body shape, age, and posture can be considered to optimize effectiveness of restraint systems.  Other automated vehicle research focuses on how occupants move relative to restraint systems during braking and vehicle manuevers, and what factors contribute to vehicle motion sickness that may be more prevalent in AVs.

Behavioral Sciences Group

Despite the rapid proliferation of AV's onto our roadways, driver decision making and behavior will continue to play a critical role in the success and safety of AV's. One important focus of the UMTRI Behavioral Sciences Group is conducting research that will help us better understand how drivers, particularly vulnerable drivers such as older adults and teens, learn about and use AV technologies and systems. In addition, researchers in the BSG are involved in research to examine factors associated with trust in AV's and how trust influences the acceptance and use of vehicle automation.

CMISST
How many miles of travel are needed for an AV manufacturer to thoroughly demonstrate that their vehicle or AV system is at least as safe as a traditional vehicle? UMTRI experts have access to petabytes of efficient and high-quality naturalistic data sets, crash data for the U.S. and several states, as well as the largest set of connected vehicle data in the world. This data will better inform manufacturers and policy makers in their pursuit of a national connected vehicle network and AV deployment. This group is also engaged in the design and optimization of shared mobility systems.

Recent Publications

 

  • Beak B, Head KL, Feng Y (2017) Adaptive coordination based on connected vehicle technology, Transportation Research Record. 2619:1-12. DOI:10.3141/2619-01
  • Bogard S, Bao S, LeBlanc D, Li J, Qiu S, Liu, B (2017) Performance of DSRC during safety pilot model deployment. SAE International Journal of Passenger Cars – Electronic and Electrical Systems 10(1):165-172. DOI:10.4271/2017-01-0077.
  • Cao H, Song X, Zhao S, Bao, S and Huang Z (2017) An optimal model-based trajectory following architecture synthesising the lateral adaptive preview strategy and longitudinal velocity planning for highly automated vehicle. Vehicle System Dynamics, 55(8), 1143-1188. DOI: 10.1080/00423114.2017.1305114
  • Cao H, Zhao S, Song Z, Bao S, Li M, Huang Z, Hu C (2018) An optimal hierarchical framework of the trajectory following by convex optimization for highly automated driving vehicles. Vehicle System Dynamics 1-31. DOI: 10.1080/00423114.2018.1497185
  • Eby DW, Molnar LJ, and Stanciu SC. (2018) Older Adults’ Attitudes and Opinions about Automated Vehicles: A Literature Review. Report No. ATLAS-2019-26. Ann Arbor, MI: University of Michigan Transportation Research Institute. ATLAS-center.org
  • Feng Y, Huang S, Chen QA, Liu HX, and Mao ZM. (2018) Vulnerability of traffic control system under cyber-attacks using falsified data. Transportation Research Record 2672(1):1-11. DOI: 10.1177/0361198118756885
  • Feng Y, Yu C, Liu HX (2018) Spatiotemporal intersection control in a connected and automated vehicle environment. Transportation Research Part C Emerging Technologies 89:364-383. DOI: 10.1016/j.trc.2018.02.001
  • Feng Y, Zamanipour M, Head KL, Khoshmagham S. (2016) Connected vehicle based adaptive signal control and applications. Transportation Research Record 2558:11-19. DOI: 10.3141/2558-02
  • Feng Y, Zheng J, and Liu HX. (2018) A real-time detector-free adaptive signal control with low penetration of connected vehicles. Transportation Research Record 2672(18):35-44. DOI: 10.1177/0361198118790860
  • Flannagan C, LeBlanc D, Bogard S, Nobukawa K, Narayanaswamy P, Leslie A, Kiefer R, Marchione M, Beck C, and Lobes K. (2016) Large-Scale Field Test of Forward Collision Alert and Lane Departure Warning Systems. Office of Advanced Safety Research, Washington, D.C. Report No. DOT HS 812 247. Nhtsa.gov
  • Flannagan CA, LeBlanc DJ, Kiefer RJ, Bogard SE, Leslie A, Zagorski CT, Zimmerman CW, Materna WS, and Beck CS. (2018) Field Study of Light-Vehicle Crash Avoidance Systems: Automatic Emergency Braking and Dynamic Brake Support (No. DOT HS 812 615). United States. Department of Transportation. National Highway Traffic Safety Administration. https://rosap.ntl.bts.gov/view/dot/38817
  • Huang Z, H Lam, DJ LeBlanc, D Zhao (2017) Accelerated evaluation of automated vehicles using piecewise mixture models. IEEE Transactions on Intelligent Transportation Systems 19(9): 2845-2855. DOI: 10.1109/TITS.2017.276617
  • Jones MLH, Ebert SM, Reed MP. (in press) Sensations associated with motion sickness response during passenger vehicle operations on a test track. SAE International Journal of Advances and Current Practices in Mobility. SAE Technical Paper 2019-01-0687. DOI:10.4271/2019-01-0687
  • Jones MLH, Le V, Ebert S, Sienko KH, Reed MP, and Sayer JR. (in press) Motion sickness in passenger vehicles during test track operations. Ergonomics
  • Li B, Zhang Y, Feng Y, Zhang Y, Ge Y, and Shao Z. (2018) Balancing computational speed and quality: a decentralized motion planning method for cooperative lane changes of connected and automated vehicles. IEEE Transactions on Intelligent Vehicles 3(3):340-350. DOI: 10.1109/TIV.2018.2843159
  • Misra A, Leslie A, Cao A, Molnar JL, Eby DW, and Flannagan C. (2018) Identifying Potential Work Zone Countermeasures Using Connected-Vehicle and Driving Data. Lansing, MI: Michigan Department of Transportation. Michigan.gov
  • Molnar LJ, Pradhan AK, Eby DW, Ryan L, St. Louis RM, Zakrajsek J, Ross B, Lin BT, Liang C, Zalewski B, and Zhang L. (2017) Age-Related Differences in Driver Behavior Associated with Automated Vehicles and the Transfer of Control between Automated and Manual Control: A Simulator Evaluation. Report No. UMTRI-2017-4. Ann Arbor, MI: University of Michigan Transportation Research Institute. https://deepblue.lib.umich.edu/handle/2027.42/137653
  • Molnar LJ, Ryan LH, Pradhan AK, Eby DW, St. Louis RM, and Zakrajsek J. (2018) Understanding trust and acceptance of automated vehicles: An exploratory simulator study of transfer of control between automated and manual driving. Transportation Research Part F: Psychology and Behaviour, 58: 319-328. DOI: 10.1016/j.trf.2018.06.004
  • Pradhan AK, Pulver E, Zakrajsek J, Bao S. and Molnar L. (2018) Perceived safety benefits, concerns, and utility of advanced driver assistance systems among owners of ADAS-equipped vehicles. Traffic Injury Prevention 19:sup2, S135-S1371. DOI: 10.1080/15389588.2018.1532201.
  • Reed MP, Ebert SM, Jones MLH, Park B-KD, Hallman JJ, and Sherony R. (2018) Passenger head kinematics in abrupt braking and lane change events. Traffic Injury Prevention 19(sup2):S70-77. DOI: 10.1080/15389588.2018.1481957
  • Sayer JR (2018) The Cost in Fatalities, Injuries and Crashes Associated with Waiting to Deploy Vehicle-to-Vehicle Communication UMTRI 2018-03 University of Michigan Transportation Research Institute https://deepblue.lib.umich.edu/handle/2027.42/147434
  • Stachowski S, Gaynier R, LeBlanc DJ (2019) An assessment method for automotive intrusion detection system performance DOT HS 812 708 National Highway Traffic Safety Administration, Washington DC. https://rosap.ntl.bts.gov/view/dot/41006
  • Stanciu S, Eby DW, Molnar LJ, St. Louis RM, and Zanier, N (2017) Interpersonal Communication and Issues for Autonomous Vehicles. Report No. ATLAS-2017-20. Ann Arbor, MI: Center for Advancing Transportation Leadership and Safety. https://rosap.ntl.bts.gov/view/dot/32338
  • Stanciu S, Eby DW, Molnar LJ, St. Louis RM, Zanier N, and Kostyniuk LP. (2018) Pedestrians/bicyclists and autonomous vehicles: How will they communicate? Transportation Research Record 2672(22):58–66. DOI: 10.1177/0361198118777091
  • Tan V, Flannagan C, and Elliott M. (2018) Predicting human-driving behavior to help driverless vehicles drive: random intercept Bayesian additive regression trees. Statistics and Its Interface 11(4): 557–572. DOI: 10.4310/SII.2018.v11.n4.a1
  • Yu C, Feng Y, Liu HX, Ma W, and Yang X. (in press) Corridor level cooperative trajectory optimization with connected and automated vehicles. Transportation Research Part C: Emerging Technologies
  • Zhang R, Cao L, Bao S and Tan J (2016) A method for connected vehicle trajectory prediction and collision warning algorithm based on V2V communication. International Journal of Crashworthiness 22(1): 15-25. DOI: 10.1080/13588265.2016.1215584
  • Zhao D, Huang X, Peng H, Lam H, and LeBlanc D. (2017) Accelerated evaluation of automated vehicles in car-following maneuvers. IEEE Transactions on Intelligent Transportation Systems 19. DOI: 10.1109/TITS.2017.2701846
  • Zhao D, Lam H, Peng H, Bao S, LeBlanc D, Nobukawa K, and Pan C. (2017) Accelerated evaluation of automated vehicles safety in lane-change scenarios based on importance sampling techniques. IEEE Transactions on Intelligent Transportation Systems 18(3):595-607. DOI: 10.1109/TITS.2016.2582208.