We propose the 1st ICML Workshop on Safe Learning for Autonomous Driving (SL4AD), as a venue for researchers in artificial intelligence to discuss research problems on autonomous driving, with a specific focus on safe learning. While there have been significant advances in vehicle autonomy (e.g., perception, trajectory forecasting, planning and control, etc.), it is of paramount importance for autonomous systems to adhere to safety specifications, as any safety infraction in urban and highway driving, or high-speed racing, could lead to catastrophic failures. We envision the workshop to bring together regulators, researchers, and industry practitioners from different AI subfields, to work towards safer and more robust autonomous technology. This workshop aims to: (i) highlight open questions about safety issues, when autonomous agents must operate in uncertain and dynamically-complex real-world environments; (ii) bring together researchers and industrial practitioners in autonomous driving with control theoreticians in safety analysis, dependability, and verification; (iii) provide a strong AI benchmark, where the joint evaluation of safety, performance, and generalisation capabilities of AD perception and control algorithms is systematically performed; (iv) provide a forum for discussion among researchers, industrial practitioners, and regulators on the core challenges, promising solution strategies, fundamental limitations, and regulatory realities involved in deploying safety-critical autonomous systems; (v) define new algorithms that handle increasingly complex real-world scenarios---where vehicles must: drive at their physical limits, where any infraction could lead to catastrophic failure, make sub-second decisions in fast-changing environments, and remain robust to distribution shifts, novel road features, and other obstacles, to facilitate cross-domain generalisation.