4,109 research outputs found
Detecting Transfer of Training Through Simulator Scenario Design: A Novice Driver Training Study
Novice drivers in comparison to experienced drivers perform poorly due to incomplete mental models of roadway hazards. This paper describes the driving simulator scenario design methods used in a novice driver training study to detect a possible transfer of training for hazard perception. Applied in a high school driver education classroom, the data of trained versus un-trained drivers is presented for pre/post-test driving scenarios, N = 67. Results showed that while general simulator control performance between the trained and un-trained groups was similar, the trained group performed better at hazard events and exhibited fewer speeding behaviors at the post-test. Specific hazard encounters indicated that simulator training may have had an effect on performance even when the training group was not trained on the specific situation. Arguments for training transfer in hazard perception are presented
Multi-level decision framework collision avoidance algorithm in emergency scenarios
With the rapid development of autonomous driving, the attention of academia
has increasingly focused on the development of anti-collision systems in
emergency scenarios, which have a crucial impact on driving safety. While
numerous anti-collision strategies have emerged in recent years, most of them
only consider steering or braking. The dynamic and complex nature of the
driving environment presents a challenge to developing robust collision
avoidance algorithms in emergency scenarios. To address the complex, dynamic
obstacle scene and improve lateral maneuverability, this paper establishes a
multi-level decision-making obstacle avoidance framework that employs the safe
distance model and integrates emergency steering and emergency braking to
complete the obstacle avoidance process. This approach helps avoid the
high-risk situation of vehicle instability that can result from the separation
of steering and braking actions. In the emergency steering algorithm, we define
the collision hazard moment and propose a multi-constraint dynamic collision
avoidance planning method that considers the driving area. Simulation results
demonstrate that the decision-making collision avoidance logic can be applied
to dynamic collision avoidance scenarios in complex traffic situations,
effectively completing the obstacle avoidance task in emergency scenarios and
improving the safety of autonomous driving
The Application of Driver Models in the Safety Assessment of Autonomous Vehicles: A Survey
Driver models play a vital role in developing and verifying autonomous
vehicles (AVs). Previously, they are mainly applied in traffic flow simulation
to model realistic driver behavior. With the development of AVs, driver models
attract much attention again due to their potential contributions to AV
certification. The simulation-based testing method is considered an effective
measure to accelerate AV testing due to its safe and efficient characteristics.
Nonetheless, realistic driver models are prerequisites for valid simulation
results. Additionally, an AV is assumed to be at least as safe as a careful and
competent driver. Therefore, driver models are inevitable for AV safety
assessment. However, no comparison or discussion of driver models is available
regarding their utility to AVs in the last five years despite their necessities
in the release of AVs. This motivates us to present a comprehensive survey of
driver models in the paper and compare their applicability. Requirements for
driver models in terms of their application to AV safety assessment are
discussed. A summary of driver models for simulation-based testing and AV
certification is provided. Evaluation metrics are defined to compare their
strength and weakness. Finally, an architecture for a careful and competent
driver model is proposed. Challenges and future work are elaborated. This study
gives related researchers especially regulators an overview and helps them to
define appropriate driver models for AVs
Safety-critical scenarios and virtual testing procedures for automated cars at road intersections
This thesis addresses the problem of road intersection safety with regard to a mixed population of automated vehicles and non-automated road users. The work derives and evaluates safety-critical scenarios at road junctions, which can pose a particular safety problem involving automated cars. A simulation and evaluation framework for car-to-car accidents is presented and demonstrated, which allows examining the safety performance of automated driving systems within those scenarios.
Given the recent advancements in automated driving functions, one of the main challenges is safe and efficient operation in complex traffic situations such as road junctions. There is a need for comprehensive testing, either in virtual testing environments or on real-world test tracks. Since it is unrealistic to cover all possible combinations of traffic situations and environment conditions, the challenge is to find the key driving situations to be evaluated at junctions.
Against this background, a novel method to derive critical pre-crash scenarios from historical car accident data is presented. It employs k-medoids to cluster historical junction crash data into distinct partitions and then applies the association rules algorithm to each cluster to specify the driving scenarios in more detail. The dataset used consists of 1,056 junction crashes in the UK, which were exported from the in-depth On-the-Spot database. The study resulted in thirteen crash clusters for T-junctions, and six crash clusters for crossroads. Association rules revealed common crash characteristics, which were the basis for the scenario descriptions.
As a follow-up to the scenario generation, the thesis further presents a novel, modular framework to transfer the derived collision scenarios to a sub-microscopic traffic simulation environment. The software CarMaker is used with MATLAB/Simulink to simulate realistic models of vehicles, sensors and road environments and is combined with an advanced Monte Carlo method to obtain a representative set of parameter combinations. The analysis of different safety performance indicators computed from the simulation outputs reveals collision and near-miss probabilities for selected scenarios. The usefulness and applicability of the simulation and evaluation framework is demonstrated for a selected junction scenario, where the safety performance of different in-vehicle collision avoidance systems is studied. The results show that the number of collisions and conflicts were reduced to a tenth when adding a crossing and turning assistant to a basic forward collision avoidance system.
Due to its modular architecture, the presented framework can be adapted to the individual needs of future users and may be enhanced with customised simulation models. Ultimately, the thesis leads to more efficient workflows when virtually testing automated driving at intersections, as a complement to field operational tests on public roads
Proactive Emergency Collision Avoidance for Automated Driving in Highway Scenarios
Uncertainty in the behavior of other traffic participants is a crucial factor
in collision avoidance for automated driving; here, stochastic metrics should
often be considered to avoid overly conservative decisions. This paper
introduces a Stochastic Model Predictive Control (SMPC) planner for emergency
collision avoidance in highway scenarios to proactively minimize collision risk
while ensuring safety through chance constraints. To address the challenge of
guaranteeing the feasibility for the emergency trajectory, we incorporate
nonlinear tire dynamics in the prediction model of the ego vehicle. Further, we
exploit Max-Min-Plus-Scaling (MMPS) approximations of the nonlinearities to
avoid conservatism, enforce proactive collision avoidance, and improve
computational efficiency in terms of performance and speed. Consequently, our
contributions include integrating a dynamic ego vehicle model into the SMPC
planner, introducing the MMPS approximation for real-time implementation in
emergency scenarios, and integrating SMPC with hybridized chance constraints
and risk minimization. We evaluate our SMPC formulation in terms of proactivity
and efficiency in various hazardous scenarios. Moreover, we demonstrate the
effectiveness of our proposed approach by comparing it with a state-of-the-art
SMPC planner and validate the feasibility of generated trajectories using a
high-fidelity vehicle model in IPG CarMaker.Comment: 13 pages, 10 figures, submitted to IEEE Transactions on Control
Systems Technolog
Automated driving and autonomous functions on road vehicles
In recent years, road vehicle automation has become an important and popular topic for research
and development in both academic and industrial spheres. New developments received
extensive coverage in the popular press, and it may be said that the topic has captured the
public imagination. Indeed, the topic has generated interest across a wide range of academic,
industry and governmental communities, well beyond vehicle engineering; these include computer
science, transportation, urban planning, legal, social science and psychology. While this
follows a similar surge of interest – and subsequent hiatus – of Automated Highway Systems
in the 1990’s, the current level of interest is substantially greater, and current expectations
are high. It is common to frame the new technologies under the banner of “self-driving cars”
– robotic systems potentially taking over the entire role of the human driver, a capability that
does not fully exist at present. However, this single vision leads one to ignore the existing
range of automated systems that are both feasible and useful. Recent developments are underpinned
by substantial and long-term trends in “computerisation” of the automobile, with
developments in sensors, actuators and control technologies to spur the new developments in
both industry and academia. In this paper we review the evolution of the intelligent vehicle
and the supporting technologies with a focus on the progress and key challenges for vehicle
system dynamics. A number of relevant themes around driving automation are explored in
this article, with special focus on those most relevant to the underlying vehicle system dynamics.
One conclusion is that increased precision is needed in sensing and controlling vehicle
motions, a trend that can mimic that of the aerospace industry, and similarly benefit from
increased use of redundant by-wire actuators
Safety Evaluation Using Counterfactual Simulations: The use of computational driver behavior models in crash avoidance systems and virtual simulations with optimal subsampling
Traffic safety is a problem worldwide. In-vehicle conflict and crash avoidance systems have been under development and assessment for some time, as integral parts of Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS). Among the methods used to assess conflict and crash avoidance systems developed by the automotive industry, virtual safety assessment methods have been shown to have great potential and efficiency. In fact, scenario generation-based virtual safety assessments play—and are likely to continue to play—a very important role in the assessments of vehicles of all levels of automation. The ultimate aim of this thesis is to improve the safety performance of conflict and crash avoidance systems. This aim is addressed through the use of computational driver models in two different ways. First, by using comfort-zone boundaries in system design, and second, by using a behavior-based crash-causation model together with a novel optimized scenario generation method for virtual safety assessment.The first objective of this thesis is to investigate how a driver model which includes road users’ comfortable behaviors in crash avoidance algorithms impacts the systems’ safety performance and the residual crash characteristics. Chinese car-to-two-wheeler crashes were targeted; Automated Emergency Braking (AEB) algorithms, which comprised the proposed crash avoidance systems, were compared to a traditional AEB algorithm. The proposed algorithms showed larger safety performance benefits. In addition, the similarities in residual crash characteristics regarding impact speed and location after different AEB implementations can potentially simplify the designs of in-crash protection system in future.The second objective is to develop and apply a method for efficient subsampling in crash-causation-model-based scenario generation for virtual safety assessment. The method, which is machine-learning-assisted, actively and iteratively updates the sampling probability based on new simulation results. The crash-causation model is based on off-road glances and a distribution of driver maximum decelerations in critical situations. A simple time-to-collision-based AEB algorithm was used to demonstrate the assessment process as well as the benefits of combining crash-causation-model-based scenario generation and optimal subsampling. The sampling methods are designed to target specific safety benefit indicators, such as impact speed reduction and crash avoidance rate. The results of the study show that the proposed sampling method requires almost 50% fewer simulations than traditional importance sampling.Future work aims to focus on applying the active sampling method to driver-model-based car-to-vulnerable road user (VRU) scenario generation. In addition to assessing conflict and crash avoidance system performance, a novel stopping criterion based on Bayesian future prediction will be further developed and demonstrated for use in experiments (e.g., as part of developing driver models) and virtual simulations (e.g., using driver-behavior-based crash-causation models). This criterion will be able to indicate when studies are unlikely to yield actionable results within the budget available, facilitating the decision to discontinue them while they are being run
Design and validation of decision and control systems in automated driving
xxvi, 148 p.En la última década ha surgido una tendencia creciente hacia la automatización de los vehículos, generando un cambio significativo en la movilidad, que afectará profundamente el modo de vida de las personas, la logística de mercancías y otros sectores dependientes del transporte. En el desarrollo de la conducción automatizada en entornos estructurados, la seguridad y el confort, como parte de las nuevas funcionalidades de la conducción, aún no se describen de forma estandarizada. Dado que los métodos de prueba utilizan cada vez más las técnicas de simulación, los desarrollos existentes deben adaptarse a este proceso. Por ejemplo, dado que las tecnologías de seguimiento de trayectorias son habilitadores esenciales, se deben aplicar verificaciones exhaustivas en aplicaciones relacionadas como el control de movimiento del vehículo y la estimación de parámetros. Además, las tecnologías en el vehículo deben ser lo suficientemente robustas para cumplir con los requisitos de seguridad, mejorando la redundancia y respaldar una operación a prueba de fallos. Considerando las premisas mencionadas, esta Tesis Doctoral tiene como objetivo el diseño y la implementación de un marco para lograr Sistemas de Conducción Automatizados (ADS) considerando aspectos cruciales, como la ejecución en tiempo real, la robustez, el rango operativo y el ajuste sencillo de parámetros. Para desarrollar las aportaciones relacionadas con este trabajo, se lleva a cabo un estudio del estado del arte actual en tecnologías de alta automatización de conducción. Luego, se propone un método de dos pasos que aborda la validación de ambos modelos de vehículos de simulación y ADS. Se introducen nuevas formulaciones predictivas basadas en modelos para mejorar la seguridad y el confort en el proceso de seguimiento de trayectorias. Por último, se evalúan escenarios de mal funcionamiento para mejorar la seguridad en entornos urbanos, proponiendo una estrategia alternativa de estimación de posicionamiento para minimizar las condiciones de riesgo
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