Accurate target parameter estimation of range, velocity, and angle is essential for vehicle safety in advanced driver assistance systems (ADAS) and autonomous vehicles. To enable spectrum sharing, ADAS may employ integrated sensing and communications (ISAC). This paper examines a dual-deconvolution automotive ISAC scenario where the radar waveform is known but the propagation channel is not, while in the communications domain, the channel is known but the transmitted message is not. Conventional maximum likelihood (ML) estimation for automotive target parameters is computationally demanding. To address this, we propose a low-complexity approach using the controlled loosening-up (CLuP) algorithm, which employs iterative refinement for efficient separation and estimation of radar targets. We achieve this through a nuclear norm restriction that stabilizes the problem. Numerical experiments demonstrate the robustness of this approach under high-mobility and noisy automotive environments, highlighting CLuP's potential as a scalable, real-time solution for ISAC in future vehicular networks.