## 1 Introduction  
Autonomous vehicles (AVs) have been attracting more and more attention and interest in both industry and academia. AVs rely on various sensors, e.g., camera, LiDAR, radar, GPS, IMU, etc., to perceive the surrounding environment and plan movement and routes [1]. To achieve autonomous driving, objects on the road should be detected accurately and quickly. The latest object-detection methods or systems mostly use deep learning for detection. Although they are more accurate, deep-learning networks are compute-intensive and require powerful computing capacity on a vehicle. Furthermore, object-detection networks are only one of the many deep-learning networks that are run on a vehicle for various autonomous driving tasks. To provide reliable computing power for delay-sensitive applications, edge computing [2] offers a cost-effective and scalable way to execute part of those deep-learning workloads for nearby vehicles. This _vehicle-edge cooperative computing_ is attractive and practical for both existing ego AVs and future connected AVs. Privacy, however, is a big concern in vehicle-edge collaboration, as the sensor data containing sensitive information leave the vehicle and are processed on an edge server. Both the sensitive information in the input data and the object results from the detection network can be accessed and used by the edge server. Existing approaches, such as homomorphic encryption [34], secure multi-party computation [5], and secret sharing approximation [6], protect data privacy at the price of a prohibitive computational overhead and/or comprised detection accuracy. To address these issues, in this paper, we present vehicle-edge cooperative deep learning with privacy preservation for object-detection tasks, named vePOD for short. vePOD aims to protect the privacy of both the sensor data from vehicles and the object results generated by CNN from being exposed to and used by edge servers which execute the CNN inference jobs. In vePOD, we leverage the additive secret sharing theory to design secure functions for every layer in an object-detection CNN. A vehicle's sensor data (e.g., a 2D image from an onboard camera) are partitioned and encrypted into two or more secret shares. Each share is transferred to and then processed on an edge server, which runs the object-detection CNN with secure functions on the secret share. The generated result from each edge server is then sent to the original vehicle (for workload offloading) or another vehicle (for workload offloading and data sharing), where the data are combined to obtain the detected objects. The entire process is configurable. When the vehicle-edge communication latency is high, a vehicle can help by executing the first several layers in the detection network and then send the secret shares of feature maps to edge servers for the remaining compute-intensive processing. This vehicle-edge cooperative computing paradigm can protect data privacy, reduce communication cost, and improve application performance. We present two use cases where the layers in object-detection CNNs--Faster R-CNN (two-stage detection) and YOLO (single-stage detection)--are enhanced by secure functions in vePOD. The vePOD version of the networks, denoted by vePOD Faster R-CNN and vePOD YOLO, are tested on the COCO dataset. Experimental results show that vePOD Faster R-CNN and vePOD YOLO achieve the same detection accuracy as Faster R-CNN and YOLO, respectively. Secure functions on secret shares protect data privacy, but prolong the detection time. The execution time of vePOD Faster R-CNN is extended from 14 s (Faster R-CNN) to 44.1 s (vePOD Faster R-CNN) without any optimization and vePOD YOLO is extended from 3.2 s to 80.7 s. Compared with a slowdown of four orders of magnitude with homomorphic encryption on CNN [7], vePOD achieves a significant speed-up, which makes vePOD practical for real-world applications. The rest of the paper is organized as follows. Section 2 describes object-detection CNNs and the detailed design of vePOD.