Development of an autonomous braking system using the predicted stopping distance

Lee, D. H.; Kim, S. K.; Kim, C. S.; Huh, Kunsoo

doi:10.1007/s12239-014-0035-5

Cited by 29 publications

(5 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this AEBS, when the TTC falls below a pre-defined threshold value (generally 0.5 to 1.5 seconds), the AEBS automatically starts to perform an emergency braking. The activation condition differs among systems, and the consequent effectiveness is different [7]. Researchers have conducted simulation and experimental tests to determine the activation condition.…”

Section: A Motivationmentioning

confidence: 99%

Robust Optimal Braking Policy for Avoiding Collision With Front Bicycle

Shen,

Zhang,

Zhang

et al. 2023

IEEE Open J. Intell. Transp. Syst.

View full text Add to dashboard Cite

Bicycles are frequently involved in traffic collisions with vehicles, particularly when sudden changes in direction occur. This paper presents a robust risk-predictive braking policy to ensure collision avoidance in all possible crossing behaviors of a bicycle. The policy controls the vehicle to follow an upper limit of the safe speed before the bicycle changes direction, ensuring that the vehicle can stop in time by the advanced emergency braking system before a collision occurs in any situation. The upper limit of the safe speed is the solution of an intractable robust optimization problem. Therefore, a scenario approach is adapted to develop a tractable approximate problem for the original robust optimization problem. The feasibility and optimality of the problem reduction are theoretically proved. A bisection method-based fast algorithm is designed to solve the approximate problem of the original robust optimization problem, making it applicable in practical scenarios. The convergence of the algorithm is also proven. The effectiveness of the proposed method is validated through hardware-in-the-loop simulations using CarMaker.

show abstract

Section: A Motivationmentioning

confidence: 99%

Robust Optimal Braking Policy for Avoiding Collision With Front Bicycle

Shen,

Zhang,

Zhang

et al. 2023

IEEE Open J. Intell. Transp. Syst.

View full text Add to dashboard Cite

show abstract

“…The dynamic model based on physical rules regards the vehicle as an object governed by physical laws. Use the physical laws of kinematics and dynamics to build predictive models [3]. Houenou et al [4] adopted the polynomial fitting method and used the fifth-order polynomial to fit the trajectory.…”

Section: Traditional Trajectory Predictionmentioning

confidence: 99%

Feature Pyramid Network Based on Skip Connect for Vehicle Prediction

Liu²,

Yang³

2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

The trajectory prediction of the target vehicle can improve the perception ability of autonomous driving vehicles in the open world environment and is an essential technology for realizing high-level fully automatic driving. The traditional vehicle trajectory prediction model based on image input is sensitive to the image style of the dataset and has weak generalization ability. The prediction model based on intermediate features can overcome the problem of different dataset styles. However, there are relatively few explorations on building models based on intermediate semantic features. Inspired by the feature pyramid in image processing, this paper proposes an FPSP (Feature Pyramid network based on Skip connect for vehicle Prediction) network. By adding the pyramid feature branch, the original network is improved. The initial coarse information is hierarchically introduced into corresponding abstract high-dimensional features. Experiments on the KITTI dataset prove that FPSP achieves considerable improvement with only a slight increase in time consumption.

show abstract

“…Among others, Model Predictive Control (MPC) has been effectively used in [24][25][26][27][28] in order to synthesize an ACC system. Regarding the AEB, typically event-based controllers have been realized through a continuous evaluation of the braking distance [29,30] or the collision time [31]. Alternative formulations can be found in [32], where the authors classify the collision risk upon the definition of potential fields, or in [33] where an impedance controller is synthesized, thus resulting in a time based controller.…”

Section: Related Workmentioning

confidence: 99%

On-Board Road Friction Estimation Technique for Autonomous Driving Vehicle-Following Maneuvers

et al. 2021

View full text Add to dashboard Cite

In recent years, autonomous vehicles and advanced driver assistance systems have drawn a great deal of attention from both research and industry, because of their demonstrated benefit in reducing the rate of accidents or, at least, their severity. The main flaw of this system is related to the poor performances in adverse environmental conditions, due to the reduction of friction, which is mainly related to the state of the road. In this paper, a new model-based technique is proposed for real-time road friction estimation in different environmental conditions. The proposed technique is based on both bicycle model to evaluate the state of the vehicle and a tire Magic Formula model based on a slip-slope approach to evaluate the potential friction. The results, in terms of the maximum achievable grip value, have been involved in autonomous driving vehicle-following maneuvers, as well as the operating condition of the vehicle at which such grip value can be reached. The effectiveness of the proposed approach is disclosed via an extensive numerical analysis covering a wide range of environmental, traffic, and vehicle kinematic conditions. Results confirm the ability of the approach to properly automatically adapting the inter-vehicle space gap and to avoiding collisions also in adverse road conditions (e.g., ice, heavy rain).

show abstract

Development of an autonomous braking system using the predicted stopping distance

Cited by 29 publications

References 2 publications

Robust Optimal Braking Policy for Avoiding Collision With Front Bicycle

Robust Optimal Braking Policy for Avoiding Collision With Front Bicycle

Feature Pyramid Network Based on Skip Connect for Vehicle Prediction

On-Board Road Friction Estimation Technique for Autonomous Driving Vehicle-Following Maneuvers

Contact Info

Product

Resources

About