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A large number of traffic collisions occur as a result of obstructed sight lines, such that even an advanced driver assistance system would be unable to prevent the crash. Recent work has proposed the use of around-the-corner radar systems to detect vehicles, pedestrians, and other road users in these occluded regions. Through comprehensive measurement, we show that these existing techniques cannot sense occluded moving objects in many important real-world scenarios. To solve this problem of limited coverage, we leverage multiple, curved reflectors to provide comprehensive coverage over the most important locations near an intersection. In scenarios where curved reflectors are insufficient, we evaluate the relative benefits of using additional flat planar surfaces. Using these techniques, we more than double the probability of detecting a vehicle near the intersection in three real urban locations, and enable NLoS radar sensing using an entirely new class of reflectors. 

Vehicle detection with visual sensors like lidar and camera is one of the critical functions enabling autonomous driving. While they generate fine-grained point clouds or high-resolution images with rich information in good weather conditions, they fail in adverse weather (e.g., fog) where opaque particles distort lights and significantly reduce visibility. Thus, existing methods relying on lidar or camera experience significant performance degradation in rare but critical adverse weather conditions. To remedy this, we resort to exploiting complementary radar, which is less impacted by adverse weather and becomes prevalent on vehicles. In this paper, we present Multimodal Vehicle Detection Network (MVDNet), a two-stage deep fusion detector, which first generates proposals from two sensors and then fuses region-wise features between multimodal sensor streams to improve final detection results. To evaluate MVDNet, we create a procedurally generated training dataset based on the collected raw lidar and radar signals from the open-source Oxford Radar Robotcar. We show that the proposed MVDNet surpasses other state-of-the-art methods, notably in terms of Average Precision (AP), especially in adverse weather conditions. The code and data are available at https://github.com/qiank10/MVDNet. 

Emerging autonomous driving systems require reliable perception of 3D surroundings. Unfortunately, current mainstream perception modalities, i.e., camera and Lidar, are vulnerable under challenging lighting and weather conditions. On the other hand, despite their all-weather operations, today's vehicle Radars are limited to location and speed detection. In this paper, we introduce MILLIPOINT, a practical system that advances the Radar sensing capability to generate 3D point clouds. The key design principle of MILLIPOINT lies in enabling synthetic aperture radar (SAR) imaging on low-cost commodity vehicle Radars. To this end, MILLIPOINT models the relation between signal variations and Radar movement, and enables self-tracking of Radar at wavelength-scale precision, thus realize coherent spatial sampling. Furthermore, MILLIPOINT solves the unique problem of specular reflection, by properly focusing on the targets with post-imaging processing. It also exploits the Radar's built-in antenna array to estimate the height of reflecting points, and eventually generate 3D point clouds. We have implemented MILLIPOINT on a commodity vehicle Radar. Our evaluation results show that MILLIPOINT effectively combats motion errors and specular reflections, and can construct 3D point clouds with much higher density and resolution compared with the existing vehicle Radar solutions.