{"id":4800,"date":"2023-07-07T15:00:11","date_gmt":"2023-07-07T07:00:11","guid":{"rendered":"https:\/\/autowise.ai\/?p=4800"},"modified":"2023-11-08T16:49:47","modified_gmt":"2023-11-08T08:49:47","slug":"autowise-launched-remote-operation-platform-radar24","status":"publish","type":"post","link":"https:\/\/autowise.ai\/en\/2023\/07\/07\/autowise-launched-remote-operation-platform-radar24\/","title":{"rendered":"Autowise.ai held Technology Open Day and launched the remote operation platform Radar 24"},"content":{"rendered":"[vc_row type=”full_width_content” full_screen_row_position=”middle” column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” scene_position=”center” top_padding=”80px” left_padding_desktop=”15%” constrain_group_2=”yes” right_padding_desktop=”15%” top_padding_tablet=”60px” left_padding_tablet=”5%” constrain_group_4=”yes” right_padding_tablet=”5%” top_padding_phone=”30px” left_padding_phone=”20px” constrain_group_6=”yes” right_padding_phone=”20px” text_color=”dark” text_align=”left” row_border_radius=”none” row_border_radius_applies=”bg” overflow=”visible” advanced_gradient_angle=”0″ overlay_strength=”0.3″ gradient_direction=”left_to_right” shape_divider_position=”bottom” bg_image_animation=”none” gradient_type=”default” shape_type=””][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” left_margin=”auto” constrain_group_2=”yes” right_margin=”auto” column_element_spacing=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_shadow=”none” column_border_radius=”none” column_link_target=”_self” max_width_desktop=”1000px” column_position=”default” advanced_gradient_angle=”0″ gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1\/1″ tablet_width_inherit=”default” tablet_text_alignment=”default” phone_text_alignment=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid” gradient_type=”default”][vc_column_text css_animation=”fadeInUp” max_width=”1000px” css=””]On July 6, Autowise.ai\u00a0held the Technology Open Day at the\u00a0World Artificial Intelligence\u00a0Conference\u00a02023. Yanye Tang, partner and vice president of product and operation of Autowise.ai, demonstrated its\u00a0technical features in the fields of perception, localization, planning\u00a0and control, and released the remote operation platform Radar 24 for the first time, which means that the operation of autonomous driving has entered the “deep end”. By solving the long-tail problem in reality, the real implementation of autonomous driving might\u00a0be realized.[\/vc_column_text][image_with_animation image_url=”4805″ image_size=”medium_large” animation_type=”entrance” animation=”Fade In” hover_animation=”none” alignment=”center” border_radius=”none” box_shadow=”none” image_loading=”default” max_width=”100%” max_width_mobile=”default” margin_bottom=”20px”][image_with_animation image_url=”4808″ image_size=”medium_large” animation_type=”entrance” animation=”Fade In” hover_animation=”none” alignment=”center” border_radius=”none” box_shadow=”none” image_loading=”default” max_width=”100%” max_width_mobile=”default” margin_bottom=”20px”][vc_column_text css_animation=”fadeInUp” max_width=”1000px”]\n

Scenario-driven research and development<\/b><\/strong>, <\/b><\/strong>continuously solv<\/b><\/strong>ing<\/b><\/strong>\u00a0the long-tail problem of autonomous driving<\/b><\/strong><\/h4>\n[\/vc_column_text][vc_column_text css_animation=”fadeInUp” max_width=”1000px”]In recent years, the sensor and R&D\u00a0solutions of autonomous driving companies are increasingly converging. In each vertical scenario, the long-tail problem also need\u00a0to\u00a0be solved.<\/p>\n

Taking sanitation autonomous\u00a0driving as an example, since sanitation vehicles are driving on urban public roads, in addition to identifying vehicles, pedestrians, traffic lights and other general objects, but also to identify garbage on the ground, roadside branches, water pipes, stones, potholes, etc., which greatly increase the number of corner cases perceived by autonomous driving. To this end, at the perception level, Autowise.ai\u00a0adopts a multi-sensor-based traditional algorithm + BEV multi-layer redundancy scheme, and uses a multi-task large model to support the detection and identification of common traffic participation and long-tail general obstacles, improving\u00a0reliability and safety.[\/vc_column_text][image_with_animation image_url=”4801″ image_size=”medium_large” animation_type=”entrance” animation=”Fade In” hover_animation=”none” alignment=”center” border_radius=”none” box_shadow=”none” image_loading=”default” max_width=”100%” max_width_mobile=”default” margin_bottom=”20px”][vc_column_text css_animation=”fadeInUp” max_width=”1000px”]In order to ensure curbside\u00a0cleaning, sanitation autonomous driving requires higher positioning accuracy. Based on HD-maps and on-site real-time positioning, Autowise.ai\u00a0realizes centimeter\u00a0level positioning, with an error within 3\u00a0cm. On\u00a0planning level, sanitation autonomous driving requires a higher degree of refinement. Autowise.ai\u00a0adopts 3D planning, the refined curbside\u00a0trajectory\u00a0based on Euclidean\u00a0coordinate system without expansion, considering the 3D shape of the vehicle (sweeping brush, protruding sensors on the vehicle, etc.) and the interaction with\u00a0roadside obstacles of different heights and materials. On control level, Automotive\u00a0Grade\u00a0EHB and SBW are used\u00a0for high response accuracy.<\/p>\n

In terms of tool chain, Autowise.ai\u00a0has built a closed-loop system. Autowise.ai\u00a0deploys triggers on the vehicle side to collect specific abnormal data on demand. Complete sensor and test data will also be sent back to the cloud. Those\u00a0data will be screened through data mining and active learning. After the data is automatically\/semi-automatically or manually marked, it enters the data platform for unified management and further\u00a0model iterations. In order to reduce the dependence on the amount of labeled data, Autowise.ai\u00a0independently developed a semi-supervised training framework based on multi-view consistency, and used massive unlabeled data to improve model performance. Related work was published in ECCV and CVPR. After passing the performance evaluation, the model will be released after model optimization and regression testing, being\u00a0deployed on\u00a0the fleet to complete the data closed loop.[\/vc_column_text][vc_column_text css_animation=”fadeInUp” max_width=”1000px”]\n

Launching Radar 24, the autonomous driving starts to compete in \u201cOperation\u201d<\/b><\/strong><\/h4>\n[\/vc_column_text][image_with_animation image_size=”full” animation_type=”entrance” animation=”Fade In” hover_animation=”none” alignment=”” border_radius=”none” box_shadow=”none” image_loading=”default” max_width=”100%” max_width_mobile=”default”][vc_column_text css_animation=”fadeInUp” max_width=”1000px”]As autonomous driving technology solutions are becoming more and more convergent, autonomous driving manufacturers have begun to compete in operation\u00a0capability. Modelled on the operation model of airport towers, Autowise.ai hasdeveloped a remote operation platform Radar 24, which made its debut on the Technology Open Day\u00a0at WAIC2023, taking its name from the meaning of “24-hour operation monitoring center”.[\/vc_column_text][image_with_animation image_url=”4811″ image_size=”medium_large” animation_type=”entrance” animation=”Fade In” hover_animation=”none” alignment=”center” border_radius=”none” box_shadow=”none” image_loading=”default” max_width=”100%” max_width_mobile=”default” margin_bottom=”20px”][vc_column_text css_animation=”fadeInUp” max_width=”1000px”]Through Radar24, the operators can achieve all-day, non-discriminatory monitoring and remote control of autonomous vehicles. Autowise.ai demonstrated a demonstration of remote moving on site. When sanitation vehicle encounters temporary traffic control, low obstacles block or illegally parked vehicles on narrow roads, there is no way to bypass. The remote operators can initiate remote commands to move the vehicle out of trouble.<\/p>\n

Currently Autowise.ai\u00a0has a number of\u00a0remote operation teams in Shanghai, Wuxi, Suzhou and other places. Remote operators need to be trained and tested for the job. Based on Radar 24 platform,\u00a0each person\u00a0can remotely manage 5-10 autonomous vehicles. In addition, for overseas customers, Autowise.ai\u00a0provides remote operation APP and related training.<\/p>\n

Radar 24 went live for internal operation in September 2022. This release is actually version 2.0 of Radar 24, which is relatively stable and refined.<\/p>\n

Autowise.ai was founded in 2017. The founding team came from Didi Autonomous Driving Department, with both technical and operational genes. Autowise.ai has more than 200 employees from top internet companies, well-known automakers, listed sanitation companies and famous universities. The core products are the autonomous sweeper Autowise V3 and the autonomous vehicle platform Roboard-X. At present, hundreds of vehicles have been deployed in more than 20 cities in China, as well as in Europe, America and the Middle East. Recently, Autowise.ai won the bid for the intelligent sanitation integration project in Xi Dong New Town, Wuxi from 2023 to 2025, with a bid amount of 140 million CNY, and will put hundreds of pre-loaded mass-produced autonomous sweeper V3 into implementation of sanitation operations. The batch operation will further dilute the cost of the supply chain and promote the large-scale implementation of autonomous driving.[\/vc_column_text][\/vc_column][\/vc_row]\n","protected":false},"excerpt":{"rendered":"

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