相关论文: Towards Efficient Hazard Identification in the Con…

This paper presents a novel approach for hazard analysis in dashcam footage, addressing the detection of driver reactions to hazards, the identification of hazardous objects, and the generation of descriptive captions. We first introduce a…

In order for autonomous vehicles to become a part of the Intelligent Transportation Ecosystem, they are required to guarantee a particular level of safety. For that to happen a safe vehicle control algorithms need to be developed, which…

Safety is one of the most important development goals for highly automated driving (HAD) systems. This applies in particular to the perception function driven by Deep Neural Networks (DNNs). For these, large parts of the traditional safety…

Industrial human-robot collaborative systems must be validated thoroughly with regard to safety. The sooner potential hazards for workers can be exposed, the less costly is the implementation of necessary changes. Due to the complexity of…

Simulation environments are good for learning different driving tasks like lane changing, parking or handling intersections etc. in an abstract manner. However, these simulation environments often restrict themselves to operate under…

Existing neural network-based autonomous systems are shown to be vulnerable against adversarial attacks, therefore sophisticated evaluation on their robustness is of great importance. However, evaluating the robustness only under the…

Safety validation of autonomous driving systems is extremely challenging due to the high risks and costs of real-world testing as well as the rarity and diversity of potential failures. To address these challenges, we train a denoising…

In light of growing attention of intelligent vehicle systems, we propose developing a driver model that uses a hybrid system formulation to capture the intent of the driver. This model hopes to capture human driving behavior in a way that…

The potential to improve road safety, reduce human driving error, and promote environmental sustainability have enabled the field of autonomous driving to progress rapidly over recent decades. The performance of autonomous vehicles has…

Increasing communication and self-driving capabilities for road vehicles lead to threats imposed by attackers. Especially attacks leading to safety violations have to be identified to address them by appropriate measures. The impact of an…

This paper studies the need for individualizing vehicular communications in order to improve collision warning systems for an N-lane highway scenario. By relating the traffic-based and communications studies, we aim at reducing highway…

This paper explores the role and challenges of Artificial Intelligence (AI) algorithms, specifically AI-based software elements, in autonomous driving systems. These AI systems are fundamental in executing real-time critical functions in…

This paper presents our submission to the COOOL competition, a novel benchmark for detecting and classifying out-of-label hazards in autonomous driving. Our approach integrates diverse methods across three core tasks: (i) driver reaction…

Safety on roads is of uttermost importance, especially in the context of autonomous vehicles. A critical need is to detect and communicate disruptive incidents early and effectively. In this paper we propose a system based on an…

Autonomous vehicles must be comprehensively evaluated before deployed in cities and highways. However, most existing evaluation approaches for autonomous vehicles are static and lack adaptability, so they are usually inefficient in…

Current technologies are unable to produce massively deployable, fully autonomous vehicles that do not require human intervention. Such technological limitations are projected to persist for decades. Therefore, roadway scenarios requiring a…

Generating safety-critical scenarios, which are crucial yet difficult to collect, provides an effective way to evaluate the robustness of autonomous driving systems. However, the diversity of scenarios and efficiency of generation methods…

Considering personalized driving preferences, a new decision-making framework is developed using a differential game approach to resolve the driving conflicts of autonomous vehicles (AVs) at unsignalized intersections. To realize human-like…

Nowadays, automobile manufacturers make efforts to develop ways to make cars fully safe. Monitoring driver's actions by computer vision techniques to detect driving mistakes in real-time and then planning for autonomous driving to avoid…

Despite the growing number of automated vehicles on public roads, operating such systems in open contexts inevitably involves incidents. Developing a defensible case that the residual risk is reduced to a reasonable (societally acceptable)…