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Vehicle dynamics and control
31 Oct 2005-
TL;DR: In this paper, the authors present a mean value model of SI and Diesel engines, and design and analysis of passive and active automotive suspension components, as well as semi-active and active suspensions.
Abstract: 1. Introduction.- 2.Lateral Vehicle Dynamics.- 3. Steering Control For Automated Lane Keeping.- 4. Longitudinal Vehicle Dynamics.- 5. Introduction to Longitudinal Control.- 6. Adaptive Cruise Control.- 7. Longitudinal Control for Vehicle Platoons.- 8. Electronic Stability Control.- 9. Mean Value Modeling Of SI and Diesel Engines.- 10. Design and Analysis of Passive Automotive Suspensions.- 11. Active Automotive Suspensions.-12. Semi-Active Suspensions.- 13. Lateral and Longitudinal Tires Forces.- 14. Tire-Road Friction Measurement on Highway Vehicles.- 15. Roll Dynamics and Rollover Prevention.- 16. Dynamics and Control of Hybrid Gas Electric Vehicles.
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14 Feb 2023
TL;DR: Li et al. as mentioned in this paper proposed an approach to detect and mitigate LiDAR spoofing attacks by leveraging the data from other neighboring vehicles, which can be readily detected by comparison from other, non-modified scans.
Abstract: Autonomous vehicles rely on LiDAR sensors to detect obstacles such as pedestrians, other vehicles, and fixed infrastructures. LiDAR spoofing attacks have been demonstrated that either create erroneous obstacles or prevent detection of real obstacles, resulting in unsafe driving behaviors. In this paper, we propose an approach to detect and mitigate LiDAR spoofing attacks by leveraging LiDAR scan data from other neighboring vehicles. This approach exploits the fact that spoofing attacks can typically only be mounted on one vehicle at a time, and introduce additional points into the victim's scan that can be readily detected by comparison from other, non-modified scans. We develop a Fault Detection, Identification, and Isolation procedure that identifies non-existing obstacle, physical removal, and adversarial object attacks, while also estimating the actual locations of obstacles. We propose a control algorithm that guarantees that these estimated object locations are avoided. We validate our framework using a CARLA simulation study, in which we verify that our FDII algorithm correctly detects each attack pattern.
DOI•
01 Jun 2023
TL;DR: In this article , a reconfigurable modeling method is proposed to simulate the TTV with an arbitrary number of trailers and hitching mode, and the planning task is formulated as a nonlinear optimal control problem, which is solved by asynchronously expanding the size of both vehicle body and obstacles.
Abstract: Trajectory planning for a tractor-trailer vehicle (TTV) is a tough task even in a static environment because of its complicated kinematic constraints. This article aims to efficiently plan an optimal trajectory with a reconfigurable model for any TTV in the static environment. A reconfigurable modeling method is first proposed to simulate the TTV with an arbitrary number of trailers and hitching mode. Then, the planning task is formulated as a nonlinear optimal control problem. However, it is too difficult to be solved and is time-consuming to find a feasible solution, let alone the optimal one. To this end, a novel guided solving method (GSM) is proposed to solve this problem progressively with great efficiency. GSM is divided into two steps. Step 1 plans rough trajectories for the TTV, taken as an initial guess for the next step. In step 2, the problem is split into a sequence of subproblems, solved by asynchronously expanding the size of both vehicle body and obstacles, speeding up the optimization process. Simulations are carried out in several complex cases, and four competitive algorithms are selected for comparison. The results show that the proposed method outperforms others in the feasibility and efficiency of problem-solving for trajectory planning. Specifically, the proposed method is feasible in all testing cases and saves up to 81.4%, 83.2%, and 55.9% solving time compared with other three methods in the 135° turn scenario, bringing the great improvement of planning efficiency.
18 Nov 2022
TL;DR: In this article , a torque vectoring control scheme based on Nonlinear Model Predictive Control (NMPC) for four-wheel drive (4WD) EVs (using in-wheel motors) is proposed to improve the stability of an EV having multiple motors.
Abstract: As the adaptation of Electric Vehicles (EVs) is the future of transportation, there is a significant need to develop control strategies for EVs. The vehicle can lose its controllability during critical maneuvers and conflicting environmental conditions. Hence, to improve the stability of an EV having multiple motors, torque vectoring becomes very essential. This paper aims to develop a torque vectoring control scheme based on Nonlinear Model Predictive Control (NMPC) for four-wheel drive (4WD) EVs (using in-wheel motors). This control scheme, while considering constraints, ensures the vehicle stability by achieving objectives such as tracking desired yaw rate, total torque demand and minimizing the rate of change of consecutive torque inputs. In addition, to minimize the computational effort of the NMPC when dealing with this kind of complex systems, a simple control-oriented model has been built and implemented within the NMPC to enhance the desired objectives. Simulations using a physical vehicle model have been carried out to evaluate the effectiveness of the proposed control approach.
08 Oct 2022
TL;DR: In this article , the authors proposed a next-generation simulation platform for platooning evaluation, which can evaluate platoon performance on the lateral dimension of the vehicle, and showed that platoon lane-change success rate drops from 0.93 to 0.03.
Abstract: Truck platooning is a promising technology in freight transport, and needs to be commercialized as earlier as possible. Therefore, the evaluation of truck platooning is in urgent needs. To conduct the truck platooning evaluation, simulation platforms play an important role. This paper proposes a next-generation simulation platform for truck platooning evaluation. The proposed platform bears the following features: i) compatibility with various platooning decision-makers and controllers; ii) modularized design for compatibility with various manufactures; iii) be able to evaluate platoon performance on the lateral dimension. The detailed results reveal that with V/C increasing from 0.25 to 1.00, the truck platoon lane-change success rate declines, dropping from 0.93 to 0.03, and the average platoon lane-change delay grows, rising from 49.72s to 216.41s.
TL;DR: In this paper , a Long Short-Term Memory (LSTM) network-based neural network model was proposed for predicting the future values of the yaw rate of a vehicle.
Abstract: Currently, electric mobility and autonomous vehicles are of top priority from safety, environmental and economic points of view. In the automotive industry, monitoring and processing accurate and plausible sensor signals is a crucial safety-critical task. The vehicle’s yaw rate is one of the most important state descriptors of vehicle dynamics, and its prediction can significantly contribute to choosing the correct intervention strategy. In this article, a Long Short-Term Memory network-based neural network model is proposed for predicting the future values of the yaw rate. The training, validating and testing of the neural network was conducted based on experimental data gathered from three different driving scenarios. The proposed model can predict the yaw rate value in 0.2 s in the future with high accuracy, using sensor signals of the vehicle from the last 0.3 s in the past. The R2 values of the proposed network range between 0.8938 and 0.9719 in the different scenarios, and in a mixed driving scenario, it is 0.9624.