Influence of Lane Width on Semi-Autonomous Vehicle Performance

[EN] In the medium-term, the number of semi-autonomous vehicles is expected to rise significantly. These changes in vehicle capabilities make it necessary to analyze their interaction with road infrastructure, which has been developed for human-driven vehicles. Current systems use artificial vision, recording the oncoming road and using the center and edgeline road markings to automatically facilitate keeping the vehicle within the lane. In addition to alignment and road markings, lane width has emerged as one of the geometric parameters that might cause disengagement and therefore must be assessed. The objective of this research was to study the impact of lane width on semi-autonomous vehicle performance. The automatic lateral control of this type of vehicle was tested along 81 lanes of an urban arterial comprising diverse widths. Results showed that the semi-autonomous system tended to fail on narrow lanes. There was a maximum width below which human control was always required-referred to as the human lane width-measuring 2.5 m. A minimum width above which automatic control was always possible-the automatic lane width-was established to be 2.75 m. Finally, a lane width of 2.72 m was found to have the same probability of automatic and human lateral control, namely the critical lane width. Following a similar methodology, these parameters could be determined for other vehicles, enhancing the interaction between autonomous vehicles and road infrastructure and thus supporting rapid deployment of autonomous technology without compromising safety.García García, A.; Camacho-Torregrosa, FJ. (2020). Influence of Lane Width on Semi-Autonomous Vehicle Performance. Transportation Research Record. 2674(9):279-286. https://doi.org/10.1177/0361198120928351S27928626749Lu, Z., Zhang, B., Feldhütter, A., Happee, R., Martens, M., & De Winter, J. C. F. (2019). Beyond mere take-over requests: The effects of monitoring requests on driver attention, take-over performance, and acceptance. Transportation Research Part F: Traffic Psychology and Behaviour, 63, 22-37. doi:10.1016/j.trf.2019.03.018Dogan, E., Rahal, M.-C., Deborne, R., Delhomme, P., Kemeny, A., & Perrin, J. (2017). Transition of control in a partially automated vehicle: Effects of anticipation and non-driving-related task involvement. Transportation Research Part F: Traffic Psychology and Behaviour, 46, 205-215. doi:10.1016/j.trf.2017.01.012Shen, S., & Neyens, D. M. (2017). Assessing drivers’ response during automated driver support system failures with non-driving tasks. Journal of Safety Research, 61, 149-155. doi:10.1016/j.jsr.2017.02.009Du, X., & Tan, K. K. (2016). Comprehensive and Practical Vision System for Self-Driving Vehicle Lane-Level Localization. IEEE Transactions on Image Processing, 25(5), 2075-2088. doi:10.1109/tip.2016.2539683Du, X., & Tan, K. K. (2015). Vision-based approach towards lane line detection and vehicle localization. Machine Vision and Applications, 27(2), 175-191. doi:10.1007/s00138-015-0735-5Favarò, F., Eurich, S., & Nader, N. (2018). Autonomous vehicles’ disengagements: Trends, triggers, and regulatory limitations. Accident Analysis & Prevention, 110, 136-148. doi:10.1016/j.aap.2017.11.00

Towards Full Automated Drive in Urban Environments: A Demonstration in GoMentum Station, California

Each year, millions of motor vehicle traffic accidents all over the world cause a large number of fatalities, injuries and significant material loss. Automated Driving (AD) has potential to drastically reduce such accidents. In this work, we focus on the technical challenges that arise from AD in urban environments. We present the overall architecture of an AD system and describe in detail the perception and planning modules. The AD system, built on a modified Acura RLX, was demonstrated in a course in GoMentum Station in California. We demonstrated autonomous handling of 4 scenarios: traffic lights, cross-traffic at intersections, construction zones and pedestrians. The AD vehicle displayed safe behavior and performed consistently in repeated demonstrations with slight variations in conditions. Overall, we completed 44 runs, encompassing 110km of automated driving with only 3 cases where the driver intervened the control of the vehicle, mostly due to error in GPS positioning. Our demonstration showed that robust and consistent behavior in urban scenarios is possible, yet more investigation is necessary for full scale roll-out on public roads.Comment: Accepted to Intelligent Vehicles Conference (IV 2017