This paper introduces a sensing-centric joint communication and millimeter-wave radar paradigm to facilitate collaboration among intelligent vehicles. We first propose a chirp waveform-based delay-Doppler quadrature amplitude modulation (DD-QAM) that modulates data across delay, Doppler, and amplitude dimensions. Building upon this modulation scheme, we derive its achievable rate to quantify the communication performance. We then introduce an extended Kalman filter-based scheme for four-dimensional (4D) parameter estimation in dynamic environments, enabling the active vehicles to accurately estimate orientation and tangential-velocity beyond traditional 4D radar systems. Furthermore, in terms of communication, we propose a dual-compensation-based demodulation and tracking scheme that allows the passive vehicles to effectively demodulate data without compromising their sensing functions. Simulation results underscore the feasibility and superior performance of our proposed methods, marking a significant advancement in the field of autonomous vehicles. Simulation codes are provided to reproduce the results in this paper: \href{https://github.com/LiZhuoRan0/2026-IEEE-TWC-ChirpDelayDopplerModulationISAC}{https://github.com/LiZhuoRan0}.

翻译：本文提出了一种以感知为中心的联合通信与毫米波雷达范式，旨在促进智能车辆间的协作。我们首先提出了一种基于啁啾波形的延迟多普勒正交幅度调制（DD-QAM），该调制方案在延迟、多普勒和幅度维度上对数据进行调制。基于此调制方案，我们推导了其可达速率以量化通信性能。随后，我们引入了一种基于扩展卡尔曼滤波的动态环境四维参数估计方案，使主动车辆能够超越传统四维雷达系统，精确估计方位和切向速度。此外，在通信方面，我们提出了一种基于双补偿的解调与跟踪方案，使被动车辆能够在不影响其感知功能的前提下有效解调数据。仿真结果验证了所提方法的可行性和优越性能，标志着自动驾驶车辆领域的重要进展。本文提供了仿真代码以复现结果：https://github.com/LiZhuoRan0/2026-IEEE-TWC-ChirpDelayDopplerModulationISAC。