RACF: A Resilient Autonomous Car Framework with Object Distance Correction
Autonomous vehicles are increasingly deployed in safety-critical applications, where sensing failures or cyberphysical attacks can lead to unsafe operations resulting in human loss and/or severe physical damages. Reliable real-time perception is therefore critically important for their safe operations and acceptability. For example, vision-based distance estimation is vulnerable to environmental degradation and adversarial perturbations, and existing defenses are often reactive and too slow to promptly mitigate their impacts on safe operations. We present a Resilient Autonomous Car Framework (RACF) that incorporates an Object Distance Correction Algorithm (ODCA) to improve perception-laye...
| Authors | Chieh Tsai, Hossein Rastgoftar, Salim Hariri |
|---|---|
| Primary category | cs.RO |
| Published | 14 April 2026 |
| Last updated at source | 14 April 2026 |
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