Rollover Prevention Control for Autonomous Electric Road Sweeper

This paper presents a method to prevent the rollover of autonomous electric road sweepers (AERS). AERS have an articulated frame steering (AFS) mechanism. Moreover, the heights of the center of gravity of the front and rear bodies are high. As such, they are prone to rolling over at low speeds and at small articulation angles. A bicycle model with a nonlinear tire model was used as a vehicle model for AERS. Using that vehicle model, path tracking and speed controllers were designed in order to follow a predefined path and speed profile, respectively. To check the rollover propensity of AERS, load transfer ratio (LTR) based the rollover analysis was completed. Based on the results of the analysis, a rollover prevention scheme was proposed. To validate the proposed scheme, a simulation was conducted using a U-shaped path under constant speed conditions. From the simulation, it was shown that the proposed scheme is effective in preventing AERS from rolling over.

Improved Anti-Lock Braking System With Real-Time Friction Detection to Maximize Vehicle Performance

Nowadays, advanced driver assistance systems play a fundamental role to improve vehicle safety and drivability; their capability to reduce the accidents rate was widely demonstrated, but these systems could also be employed to improve vehicle performance if incorporated with other control logics. This work presents an evolved version of the anti-lock braking system, obtained thanks to the combined use of a bicycle model, capable to estimate the actual friction coefficient in different environmental conditions, and a potential friction estimator based on a Magic Formula tire model with a slip-slope approach. With the presented ABS, virtually tested in several conditions, it is possible to reduce the braking distance with the final aim of reducing the braking time and, in this way, improving the vehicle performance.

Local Trajectory Planning for Autonomous Racing Vehicles Based on the Rapidly-Exploring Random Tree Algorithm

This paper presents a local trajectory planning method based on the Rapidly-exploring Random Tree (RRT) algorithm using Dubins curves for autonomous racing vehicles. The purpose of the investigated method is the real-time computation of a trajectory that could be feasible in autonomous driving. The vehicle is considered as a three Degree-of-Freedom bicycle model and a Model Predictive Control (MPC) algorithm is implemented to control the lateral and longitudinal vehicle dynamics. The trajectory planning algorithm exploits a perception pipeline using a LiDAR sensor that is mounted onto the front wing of the racing vehicle. The MPC computes the acceleration/ deceleration command and the front wheel steering angle to follow the predicted trajectory. The trajectory and control algorithms are tested on real data acquisition performed on-board the vehicle. For validation purposes, the vehicle is driven autonomously during different maneuvers performed in the racing environment that is structured with traffic cones. The feasibility of the algorithm is evaluated in terms of error with respect to the planned trajectory, tracking velocity and maximum longitudinal acceleration. The effectiveness of the method is also evaluated with respect to command signals for the steering and acceleration actuators featured by the retained racing vehicle. The results demonstrate that the trajectory is well-tracked and the signals are compatible with the actuator constraints.

Self-Steering Characteristics of FSAE Vehicle with a Linear Bicycle Model

A FSAE car must exhibit precise and predictable handling behaviour since it is subject to driving manoeuvres in dynamic conditions. Therefore, an accurate prediction of its self-steering characteristics becomes vitally important, especially in the expected lateral acceleration operating range. The simulation implements a linear bicycle model of FSAE car in MATLAB and establishes the understeer gradient and the critical speed, thereby aiding the analysis of the steering wheel angle variation required to negotiate the corners of increasing dynamics.

Conflict and Sensitivity Analysis of Vehicular Stability Using a Two-State Linear Bicycle Model

Vehicle parameters and operation conditions play a critical role in vehicular handling and stability. This study aimed to evaluate vehicle stability based on cornering tire stiffness integrated with vehicle parameters. A passenger vehicle is considered in which a two-state linear bicycle model is developed in the Matlab/Simulink. The effect of the vehicle parameters on lateral vehicle stability has been investigated and analyzed. The investigated parameters included CG longitudinal position, wheelbase, and tire cornering stiffness. Furthermore, the effects of load variation and vehicle speed were addressed. Based on a Fishhook steering maneuver, the lateral stability criteria represented in lateral acceleration, yaw rate, vehicle sideslip angle, tire sideslip angles, and the lateral tire force were analyzed. The results demonstrated that the parameters that affect the lateral vehicle stability the most are the cornering stiffness coefficient and the CG longitudinal location. The findings also indicated a positive correlation between vehicle properties and lateral handling and stability.

How Big Is My Carbon Footprint? Understanding Young People’s Engagement with Climate Change Education

This paper presents a new engagement model for climate change education (CCE) as a result of analysing interactive digital narratives (IDNs) created during the You and CO2 Climate Change Education Programme. Young people aged 13–15 from two schools in Wales participated in three workshops, which culminated in students producing IDNs about climate change using Twine storytelling software. An inductive, grounded-theory approach informed by Bourdieusien principles of habitus and value was used to explore students’ responses to the Programme. Stage 1 coding identified ‘Core Themes’ and located student responses along tri-axial continua showing engagement, agency, and power. Stage 2 coding combined ‘Core Themes’ to build upon Cantell et al.’s 2019 Bicycle Model of Climate Change Education to create a new ‘holistic Agentic Climate-Change Engagement’ model (h-ACE), where learners’ journeys towards full engagement with and understanding of CCE and action could be traced. Barriers to students’ engagement with and understanding of CCE were identified through Bourdieusien analysis of responses. Results show that engagement was related to children’s views on their capacity to effect change on individual, local and governmental levels. The h-ACE provides a model for adjusting CCE curricula to accommodate young people’s varying cultures and views.

Sensitivity Analysis of Vehicle Parameters on Dynamic Stability and Vehicle Handling Performance

In this paper, a linear two degrees of freedom linear bicycle model is proposed to investigate the vehicle handling criterion. The study is based on simulation developed using MATLAB / Simulink to predict the vehicle dynamic stability. Steering angle is given as an input to the mathematical model for various vehicular manoeuvres. This model is validated using a step input which is adjusted to give 0.3g lateral acceleration. The system model is simulated under a typical front wheel steering to examine the highway vehicle prediction output within its manoeuvre. This input is also adjusted to keep lateral acceleration value in steady state region. It is found that changing the vehicle center of gravity (CG) position, vehicle mass, tire cornering stiffness and vehicle speed all have a significant influence on the vehicle dynamic stability.