An Active Safety System for Low-Speed Bus Braking Assistance

Accidents in which buses or coaches are involved cause thousands of injuries and fatalities every year. To reduce their number and severity, the paper describes an Advanced Driver Assistance Systems (ADAS) based on a haptic throttle pedal and emergency braking. It also proposes a computationally efficient algorithm with a methodology based on three main concepts: a simplified but accurate vehicle model; an efficient collision detection system considering driver's intention and pedestrians wandering around the vehicle; and a risk evaluation system to generate warnings and emergency braking signals. Finally, the performance of the proposed ADAS is validated using a driving simulation cabin with a very realistic urban scenario and original elements from real buses. The results show a statistically significant improvement in safety, as the number of collisions and high risk situations are clearly minimized, reaction time to press the brake pedal is improved and time to collision increased in emergency situations. Implementation of the proposed ADAS into city buses would potentially improve safety, reducing the frequency and severity of accidents with pedestrians.This work was supported in part by Ministry of Science and Innovation of Spain through the SAFEBUS Project "Sistemas Avanzados de Seguridad Integral en Autobuses" under Grant IPT-2011-1165-370000 and the SAFETRANS Project "Sistemas de Conduccion Segura de Vehiculos de Transporte de Pasajeros y Materiales con Asistencia Haptica/Audiovisual e Interfaces Biomedicas" under Grant DPI2013-42302-R and in part by the Generalitat Valenciana, Programa VALi+d (ACIF/2010/206). The Associate Editor for this paper was E. Kosmatopoulos.Girbés, V.; Armesto Ángel, L.; Dols Ruiz, JF.; Tornero Montserrat, J. (2017). An Active Safety System for Low-Speed Bus Braking Assistance. IEEE Transactions on Intelligent Transportation Systems. 18(2):377-387. https://doi.org/10.1109/TITS.2016.2573921S37738718

Analysis of Pedestrian Safety Using Micro-simulation and Driving Simulator

In recent years, traffic agencies have begun to place emphasis on the importance of pedestrian safety. In the United States, nearly 70,000 pedestrians were reported injured in 2015. Although the number only account for 3% of all the people injured in traffic crashes, the number of pedestrian fatalities is still around 15% of total traffic fatalities. Furthermore, the state of Florida has consistently ranked as one of the worst states in terms of pedestrian crashes, injuries and fatalities. Therefore, it is befitting to focus on the pedestrian safety. This dissertation mainly focused on pedestrian safety at both midblock crossings and intersections by using micro-simulation and driving simulator. First, this study examined if the micro-simulation models (VISSIM and SSAM) could estimate pedestrian-vehicle conflicts at signalized intersections. A total of 42 video-hours were recorded at seven signalized intersections for field data collection. The observed conflicts from the field were used to calibrate VISSIM and replicate the conflicts. The calibrated and validated VISSIM model generated the pedestrian-vehicle conflicts from SSAM software using the vehicle trajectory data in VISSIM. The mean absolute percent error (MAPE) was used to determine the optimum TTC and PET thresholds for pedestrian-vehicle conflicts and linear regression analysis was used to study the correlation between the observed and simulated conflicts at the established thresholds. The results indicated the highest correlation between the simulated and observed conflicts when the TTC parameter was set at 2.7 and the PET was set at 8. Second, the driving simulator experiment was designed to assess pedestrian safety under different potential risk factors at both midblock crossings and intersections. Four potential risk factors were selected and 67 subjects participated in this experiment. In order to analyze pedestrian safety, the surrogate safety measures were examined to evaluate these pedestrian-vehicle conflicts. Third, by using the driving simulator data from the midblock crossing scenario, typical examples of drivers\u27 deceleration rate and the distance to crosswalk were summarized, which exhibited a clear drivers\u27 avoidance pattern during the vehicle pedestrian conflicts. This pattern was summarized into four stages, including the brake response stage, the deceleration adjustment stage, the maximum deceleration stage, and the brake release stage. In addition, the pedestrian-vehicle conflict prediction model was built to predict the minimum distance between vehicle and pedestrian. Finally, this study summarized the three different kinds of data that were to evaluate the pedestrian safety, including field data, simulation data, and driving simulator data. The process of combining of field data, simulation data, and simulator data was proposed. The process would show how the researches could evaluate the pedestrian safety by using the field observations, micro-simulation, and driving simulator