Related papers: A Language for Autonomous Vehicles Testing Oracles
We introduce Autoverse, an evolvable, domain-specific language for single-player 2D grid-based games, and demonstrate its use as a scalable training ground for Open-Ended Learning (OEL) algorithms. Autoverse uses cellular-automaton-like…
This work in progress considers reachability-based safety analysis in the domain of autonomous driving in multi-agent systems. We formulate the safety problem for a car following scenario as a differential game and study how different…
Current autonomous vehicle (AV) simulators are built to provide large-scale testing required to prove capabilities under varied conditions in controlled, repeatable fashion. However, they have certain failings including the need for user…
In this paper, we present ViSTA, a framework for Virtual Scenario-based Testing of Autonomous Vehicles (AV), developed as part of the 2021 IEEE Autonomous Test Driving AI Test Challenge. Scenario-based virtual testing aims to construct…
Recent research on Large Language Models for autonomous driving shows promise in planning and control. However, high computational demands and hallucinations still challenge accurate trajectory prediction and control signal generation.…
For a foreseeable future, autonomous vehicles (AVs) will operate in traffic together with human-driven vehicles. Their planning and control systems need extensive testing, including early-stage testing in simulations where the interactions…
With the rise of vision-language models (VLM), their application for autonomous driving (VLM4AD) has gained significant attention. Meanwhile, in autonomous driving, closed-loop evaluation has become widely recognized as a more reliable…
Full-stack autonomous driving system spans diverse technological domains-including perception, planning, and control-that each require in-depth research. Moreover, validating such technologies of the system necessitates extensive supporting…
Augmenting automated vehicles to wirelessly detect and respond to external events before they are detectable by onboard sensors is crucial for developing context-aware driving strategies. To this end, we present an automated vehicle…
The present cross-disciplinary research explores pedestrian-autonomous vehicle interactions in a safe, virtual environment. We first present contemporary tools in the field and then propose the design and development of a new application…
Although many research vehicle platforms for autonomous driving have been built in the past, hardware design, source code and lessons learned have not been made available for the next generation of demonstrators. This raises the efforts for…
Autonomous racing demands safe control of vehicles at their physical limits for extended periods of time, providing insights into advanced vehicle safety systems which increasingly rely on intervention provided by vehicle autonomy.…
Intelligent mechanisms implemented in autonomous vehicles, such as proactive driving assist and collision alerts, reduce traffic accidents. However, verifying their correct functionality is difficult due to complex interactions with the…
Real-world autonomous driving, particularly in urban environments with numerous corner cases, requires rigorous testing to ensure product safety and robustness. However, few studies have explored integrating adversarial scenario generation…
Solving complex real-world tasks requires cycles of actions and observations. This is particularly true in science, where tasks require many cycles of analysis, tool use, and experimentation. Language agents are promising for automating…
Auto manufacturers and research groups are working on autonomous driving for long period and achieved significant progress. Autonomous vehicles (AV) are expected to transform road traffic reduction from current conditions, avoiding…
The considerable effort of writing requirements is only worthwhile if the result meets two conditions: the requirements reflect stakeholders' needs, and the implementation satisfies them. In usual approaches, the use of different notations…
Unresolved questions about how autonomous vehicles (AVs) should meet the informational needs of riders hinder real-world adoption. Complicating our ability to satisfy rider needs is that different people, goals, and driving contexts have…
Integrating an automated driving software stack into vehicles with variable configuration is challenging, especially due to different hardware characteristics. Further, to provide software updates to a vehicle fleet in the field, the…
Explainable AI, in the context of autonomous systems, like self-driving cars, has drawn broad interests from researchers. Recent studies have found that providing explanations for autonomous vehicles' actions has many benefits (e.g.,…