Zobrazeno 1 - 10
of 91
pro vyhledávání: '"Redmill, Keith"'
This paper focuses on safety performance testing and characterization of black-box highly automated vehicles (HAV). Existing testing approaches typically obtain the testing outcomes by deploying the HAV into a specific testing environment. Such a tes
Externí odkaz:
http://arxiv.org/abs/2402.01576
Recent research in pedestrian simulation often aims to develop realistic behaviors in various situations, but it is challenging for existing algorithms to generate behaviors that identify weaknesses in automated vehicles' performance in extreme and u
Externí odkaz:
http://arxiv.org/abs/2306.07525
The capability to follow a lead-vehicle and avoid rear-end collisions is one of the most important functionalities for human drivers and various Advanced Driver Assist Systems (ADAS). Existing safety performance justification of the car-following sys
Externí odkaz:
http://arxiv.org/abs/2202.08935
A connected and automated vehicle safety metric determines the performance of a subject vehicle (SV) by analyzing the data involving the interactions among the SV and other dynamic road users and environmental features. When the data set contains onl
Externí odkaz:
http://arxiv.org/abs/2111.07769
Publikováno v:
IEEE Robotics and Automation Letters, vol. 7, no. 1, pp. 199-206, Jan. 2022
A typical scenario-based evaluation framework seeks to characterize a black-box system's safety performance (e.g., failure rate) through repeatedly sampling initialization configurations (scenario sampling) and executing a certain test policy for sce
Externí odkaz:
http://arxiv.org/abs/2110.02331
Avoiding unseen or partially occluded vulnerable road users (VRUs) is a major challenge for fully autonomous driving in urban scenes. However, occlusion-aware risk assessment systems have not been widely studied. Here, we propose a pedestrian emergen
Externí odkaz:
http://arxiv.org/abs/2107.02326
How many scenarios are sufficient to validate the safe Operational Design Domain (ODD) of an Automated Driving System (ADS) equipped vehicle? Is a more significant number of sampled scenarios guaranteeing a more accurate safety assessment of the ADS?
Externí odkaz:
http://arxiv.org/abs/2104.09595
Predicting vulnerable road user behavior is an essential prerequisite for deploying Automated Driving Systems (ADS) in the real-world. Pedestrian crossing intention should be recognized in real-time, especially for urban driving. Recent works have sh
Externí odkaz:
http://arxiv.org/abs/2104.05485
In mixed traffic scenarios, a certain number of pedestrians might coexist in a small area while interacting with vehicles. In this situation, every pedestrian must simultaneously react to the surrounding pedestrians and vehicles. Analytical modeling
Externí odkaz:
http://arxiv.org/abs/2101.03554
Learned pointcloud representations do not generalize well with an increase in distance to the sensor. For example, at a range greater than 60 meters, the sparsity of lidar pointclouds reaches to a point where even humans cannot discern object shapes
Externí odkaz:
http://arxiv.org/abs/2011.01404