about the title

Author: YTEP-ZHI

README.md

@@ -41,6 +41,7 @@ This repository will host the code of UniAD.
 ## News
 
 - Code & model release: We are actively re-organizing the codebase for better readability. The estimated time is late March. Please stay tuned!
+- About the title: To avoid confusion about the "goal", we change the title from "Goal-oriented" to "Planning-oriented" as suggested by the reviewers. We originally use "goal" to indicate the final safe planning in an AD pipeline, rather than "goal-point" -- the destination of a sequence of actions.
 - [2023/03/21] :rocket::rocket: UniAD paper is accepted by CVPR 2023, as an **award candidate** (12 out of 9155 submissions and 2360 accepted papers)!
 - [2022/12/21] UniAD [paper](https://arxiv.org/abs/2212.10156) is available on arXiv!
 

index.html

@@ -275,24 +275,7 @@ <h1 class="title is-1 publication-title">Planning-oriented Autonomous Driving</h
                     <h2 class="title is-3">Abstract</h2>
                     <div class="content has-text-justified">
                         <p>
-                            Modern autonomous driving system is characterized as modular tasks in sequential order,
-                            i.e., perception, prediction and planning. As sensors and hardware get improved, there is
-                            trending popularity to devise a system that can perform a wide diversity of tasks to fulfill
-                            higher-level intelligence. Contemporary approaches resort to either deploying standalone
-                            models for individual tasks, or designing a multi-task paradigm with separate heads. These
-                            might suffer from accumulative error or negative transfer effect. Instead, we argue that a
-                            favorable algorithm framework should be devised and optimized in pursuit of the ultimate
-                            goal, i.e. planning of the self-driving-car. Oriented at this goal, we revisit the key
-                            components within perception and prediction. We analyze each module and prioritize the tasks
-                            hierarchically, such that all these tasks contribute to planning (the goal). To this end, we
-                            introduce Unified Autonomous Driving (UniAD), the first comprehensive framework up-to-date
-                            that incorporates full-stack driving tasks in one network. It is exquisitely devised to
-                            leverage advantages of each module, and provide complementary feature abstractions for agent
-                            interaction from a global perspective. Tasks are communicated with unified query design to
-                            facilitate each other toward planning. We instantiate UniAD on the challenging nuScenes
-                            benchmark. With extensive ablations, the effectiveness of using such a philosophy is proven
-                            to surpass previous state-of-the-arts by a large margin in all aspects. The full suite of
-                            codebase and models would be available to facilitate future research in the community.
+                            Modern autonomous driving system is characterized as modular tasks in sequential order, i.e., perception, prediction, and planning. In order to perform a wide diversity of tasks and achieve advanced-level intelligence, contemporary approaches either deploy standalone models for individual tasks, or design a multi-task paradigm with separate heads. However, they might suffer from accumulative errors or deficient task coordination. Instead, we argue that a favorable framework should be devised and optimized in pursuit of the ultimate goal, i.e., planning of the self-driving car. Oriented at this, we revisit the key components within perception and prediction, and prioritize the tasks such that all these tasks contribute to planning. We introduce Unified Autonomous Driving (UniAD), a comprehensive framework up-to-date that incorporates full-stack driving tasks in one network. It is exquisitely devised to leverage advantages of each module, and provide complementary feature abstractions for agent interaction from a global perspective. Tasks are communicated with unified query interfaces to facilitate each other toward planning. We instantiate UniAD on the challenging nuScenes benchmark. With extensive ablations, the effectiveness of using such a philosophy is proven by substantially outperforming previous state-of-the-arts in all aspects. Code and models are public.
                         </p>
                     </div>
                 </div>