Team Cornell's Skynet: Robust perception and planning in an urban environment

A Psychological Approach to ‘Public Perception’ of Land-Use Planning: A Case Study of Jiangsu Province, China

Public perception and attitudes towards public affairs in the USA since the 1960s become a societal issue of growing importance in the field of planning. Good land-use planning should deliver a bright future vision in a way that unites and inspires groups to implement it. The introduction of public perception into planning helps to understand the process of how the public develop their awareness, value judgments, behavior and attitudes. In this research, we built the framework of public perception in land-use planning based on the affect, behavior, cognition (ABC) theory of consumer behavior. We gathered empirical data for Jiangsu province in China. We used structural equation modeling, a commonly used statistical analysis method for examining the structural relationship between multiple variables. We found that the public perception towards public affairs contributed to forming a multiple iterative interaction effect, which evolves a process from primary cognition to knowledge extraction, internalized absorption, emotional judgement and finally externalization into a certain attitudes and behaviors. On the cognitive level, our research result showed that public expectation and perceived quality have opposite effects on perceived difference and the public expectation is more influential. If the planning vision provides a clear and convincing picture of the future, and the information of planning is easy to understand, the public’s cognition and emotion can be well integrated. The core element of the emotional level is perceived value. The public is more concerned about a new planning project if it can add the value to the land, protect community environment, and improve the condition of low-income and minority populations. On the behavior level, public continuous behavior intentions could enhance perceived value, subjective norms and perceived availability. The research could further account for the root of public attitudes and behavior. This is crucial to China's land-use policy, and may well provide important lessons for other developing countries.

Autonomous driving in urban environments: approaches, lessons and challenges

The development of autonomous vehicles for urban driving has seen rapid progress in the past 30 years. This paper provides a summary of the current state of the art in autonomous driving in urban environments, based primarily on the experiences of the authors in the 2007 DARPA Urban Challenge (DUC). The paper briefly summarizes the approaches that different teams used in the DUC, with the goal of describing some of the challenges that the teams faced in driving in urban environments. The paper also highlights the long-term research challenges that must be overcome in order to enable autonomous driving and points to opportunities for new technologies to be applied in improving vehicle safety, exploiting intelligent road infrastructure and enabling robotic vehicles operating in human environments.

Path Planning for Autonomous Vehicles in Unknown Semi-structured Environments

We describe a practical path-planning algorithm for an autonomous vehicle operating in an unknown semi-structured (or unstructured) environment, where obstacles are detected online by the robot’s sensors. This work was motivated by and experimentally validated in the 2007 DARPA Urban Challenge, where robotic vehicles had to autonomously navigate parking lots. The core of our approach to path planning consists of two phases. The first phase uses a variant of A* search (applied to the 3D kinematic state space of the vehicle) to obtain a kinematically feasible trajectory. The second phase then improves the quality of the solution via numeric non-linear optimization, leading to a local (and frequently global) optimum. Further, we extend our algorithm to use prior topological knowledge of the environment to guide path planning, leading to faster search and final trajectories better suited to the structure of the environment. We present experimental results from the DARPA Urban Challenge, where our robot demonstrated near-flawless performance in complex general path-planning tasks such as navigating parking lots and executing U-turns on blocked roads. We also present results on autonomous navigation of real parking lots. In those latter tasks, which are significantly more complex than the ones in the DARPA Urban Challenge, the time of a full replanning cycle of our planner is in the range of 50—300 ms.