DRIVER RESPOND TO PRE-COLLISION SCENARIO AT INTERSECTION IN AUTONOMOUS VEHICLE

Research on automation system development is attracting a lot of attention from researchers, automotive industry manufacturers and leading technology brands. This study focus is on SAE level 3, the vehicle steering, accelerator pedal and brake pedal are controlled autonomously. The decrement in controlling vehicle and driving task has the possibility to reduce the road crash resulted in an essential change in driver role from active to passive. The effect of role change leads to decrement of situation awareness and reduce driver abilities to control manual vehicle at the right time and manner. Therefore, research on recognition times in complex and actual situations are critical. The primary purpose was to analyze the driver’s ability to recognize pedestrian, bicycle and vehicle pre-collision at intersection in the automated vehicle. The road conditions were complicated and imitated a real driving scenario. The statistical tools used for analysis were the F-test, t-test and ANOVA method. This finding shows that the subject can instantaneously recognize unintended acceleration at a low velocity and relative velocity in a pre-collision scenario with pedestrian. The implication of these results is in developing an automated vehicle system related to driver recognition. These findings provide insights that can be useful in developing autonomous vehicles

Advances in centerline estimation for autonomous lateral control

The ability of autonomous vehicles to maintain an accurate trajectory within their road lane is crucial for safe operation. This requires detecting the road lines and estimating the car relative pose within its lane. Lateral lines are usually retrieved from camera images. Still, most of the works on line detection are limited to image mask retrieval and do not provide a usable representation in world coordinates. What we propose in this paper is a complete perception pipeline based on monocular vision and able to retrieve all the information required by a vehicle lateral control system: road lines equation, centerline, vehicle heading and lateral displacement. We evaluate our system by acquiring data with accurate geometric ground truth. To act as a benchmark for further research, we make this new dataset publicly available at http://airlab.deib.polimi.it/datasets/.Comment: Presented at 2020 IEEE Intelligent Vehicles Symposium (IV), 8 pages, 8 figure

Behavioural parameters for CAVs

This document was created as part of the Levitate project. The purpose of this document is to define the Connected and Autonomous Vehicle (CAV) parameter sets for driving logics that are used in the Levitate project. The behaviour parameter sets are based on the microscopic traffic simulation software Aimsun Next (Aimsun, 2021). The assumptions on CAV parameters and their values were based on a comprehensive literature review, including both empirical and simulation-based studies (e.g., Cao et al., 2017; Eilbert et al., 2019; Goodall yet al., 2020; de Souza et al., 2021; Shladover et al., 2012), as well as discussions in meetings with experts, conducted as part of Levitate project