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Journal ArticleDOI

Situation Assessment of an Autonomous Emergency Brake for Arbitrary Vehicle-to-Vehicle Collision Scenarios

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TLDR
A new approach for the calculation of the trigger time of an emergency brake that simultaneously considers all physically possible trajectories of the object and host vehicle and the orientation of the vehicles is incorporated into the collision estimation.
Abstract
The autonomous emergency brake (AEB) is an active safety function for vehicles which aims to reduce the severity of a collision. An AEB performs a full brake when an accident becomes unavoidable. Even if this system cannot, in general, avoid the accident, it reduces the energy of the crash impact and is therefore referred to as a collision mitigation system. A new approach for the calculation of the trigger time of an emergency brake will be presented. The algorithm simultaneously considers all physically possible trajectories of the object and host vehicle. It can be applied to all different scenarios including rear-end collisions, collisions at intersections, and collisions with oncoming vehicles. Thus, 63% of possible accidents are addressed. The approach accounts for the object and host vehicles' dimensions. Unlike previous work, the orientation of the vehicles is incorporated into the collision estimation.

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Citations
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Journal ArticleDOI

A survey on motion prediction and risk assessment for intelligent vehicles

TL;DR: This paper points out the tradeoff between model completeness and real-time constraints, and the fact that the choice of a risk assessment method is influenced by the selected motion model.
Journal ArticleDOI

Probabilistic Analysis of Dynamic Scenes and Collision Risks Assessment to Improve Driving Safety

TL;DR: The article deals with the analysis and interpretation of dynamic scenes typical of urban driving, to assess risks of collision for the ego-vehicle with the use of Hidden Markov Models and Gaussian processes.
Journal ArticleDOI

Cooperative Collision Avoidance at Intersections: Algorithms and Experiments

TL;DR: This work provides an experimental validation of the formal control theoretic methods to guarantee a collision-free (safe) system, whereas overrides are only applied when necessary to prevent a crash.
Journal ArticleDOI

Model-Based Threat Assessment for Avoiding Arbitrary Vehicle Collisions

TL;DR: A model-based algorithm that estimates how the driver of a vehicle can either steer, brake, or accelerate to avoid colliding with an arbitrary object and is computationally efficient and can be used to assist the driver in avoiding or mitigating collisions with all types of road users in all kinds of traffic scenarios.
Journal ArticleDOI

Situation Assessment for Automatic Lane-Change Maneuvers

TL;DR: A system that can perceive the vehicle's environment, assess the traffic situation, and give recommendations about lane-change maneuvers to the driver and an approach that is based on Bayesian networks for taking maneuver decisions under uncertainty are presented.
References
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Journal ArticleDOI

Research advances in intelligent collision avoidance and adaptive cruise control

TL;DR: This paper explains the initiatives for automation in different levels of transportation system with a specific emphasis on the vehicle-level automation, and the impact of automation/warning systems on each of the above-mentioned factors.
Proceedings ArticleDOI

Monte Carlo road safety reasoning

TL;DR: This paper presents a framework for reasoning about the future motion of multiple objects in a road scene that can be used to either control the car, or to display warnings for the driver.
Journal ArticleDOI

A Multilevel Collision Mitigation Approach—Its Situation Assessment, Decision Making, and Performance Tradeoffs

TL;DR: A multilevel collision mitigation approach that allows a flexible tradeoff between potential benefit and the risk associated with driver acceptability and product liability is presented and algorithms that allow for an efficient incorporation of both sensor and prediction uncertainties are outlined.
Journal ArticleDOI

Sensor Fusion for Predicting Vehicles' Path for Collision Avoidance Systems

TL;DR: The proposed approach is a hierarchical-structured algorithm that fuses traffic environment data with car dynamics in order to accurately predict the trajectory of the ego-vehicle, allowing the active safety system to inform, warn the driver, or intervene when critical situations occur.
Proceedings ArticleDOI

Decision Making for Collision Avoidance Systems

TL;DR: This work focuses on forward collision by braking, and presents a general method for calculating the risk for collision, and describes results from a simulation study using a large number of scenarios created from extensive accident statistics.
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