# US 2022/0032941 A1 · Bump alert > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2022/0032941 A1 - Document ID: US20220032941A1 - Status: Published application - Kind: A1 - Application number: 17/176,136 - Filed: 2021-02-15 - Published: 2022-02-03 - Earliest claimed: 2020-08-03 - Assignees: Cartica Ai Ltd - Applicants: Cartica Ai Ltd - Inventors: Boaz Mell; Igal Raichelgauz - Web page: https://patentgrep.com/patent/US20220032941A1 ## CPC - B60W 50/14: Means for informing the driver, warning the driver or prompting a driver intervention - G06V 20/58: Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads - B60W 30/143: {Speed control (B60W30/16 takes precedence)} - B60W 40/06: Road conditions - G06F 18/24: Classification techniques - G06N 3/045: Combinations of networks - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/0495: Quantised networks; Sparse networks; Compressed networks - G06N 3/082: modifying the architecture, e.g. adding, deleting or silencing nodes or connections - G06N 3/09: Supervised learning - G06N 5/04: Inference or reasoning models - G06N 20/00: Machine learning - G06V 10/454: {Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]} - G06V 10/757: {Matching configurations of points or features} - G06V 10/774: Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting - G06V 10/82: using neural networks - G06V 20/49: {Segmenting video sequences, i.e. computational techniques such as parsing or cutting the sequence, low-level clustering or determining units such as shots or scenes} - G06V 20/56: exterior to a vehicle by using sensors mounted on the vehicle - B60W 2420/403: Image sensing, e.g. optical camera - B60W 2420/408: Radar; Laser, e.g. lidar - B60W 2420/54: Audio sensitive means, e.g. ultrasound - B60W 2554/20: Static objects - B60W 2554/80: Spatial relation or speed relative to objects - G06V 20/44: {Event detection} - B60W: CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A method for bump alert, the method may include receiving by a vehicle computerized system, at least one visual bump indicator that is visible before driving over at least one bump; obtaining sensed information regarding an environment of the vehicle; processing the sensed information, wherein the processing comprises searching a visual bump indicator of the at least one visual bump indicator; determining whether the vehicle approaches a bump; and generating the bump alert when determining that the vehicle approaches the bump. ## Claims ### Claim 1 A method for bump alert, the method comprises: receiving by a vehicle computerized system, at least one visual bump indicator that is visible before driving over at least one bump; obtaining sensed information regarding an environment of the vehicle; processing the sensed information, wherein the processing comprises searching a visual bump indicator of the at least one visual bump indicator; determining whether the vehicle approaches a bump; and generating the bump alert when determining that the vehicle approaches the bump. ### Claim 2 (depends on 1) The method according to claim 1, wherein the at least one visual bump indicator was generated by a generation process that comprises: obtaining video information and telemetric information, wherein the video information and the telemetric information are obtained during driving sessions of one or more vehicles; determining, based at least one the telemetric information, multiple suspected driving over bump events; selecting video information segments, wherein each selected video information segment is acquired before a suspected driving over bump event and in timing proximity to the suspected driving over bump event; and applying a machine learning process on the selected video information segments to find the at least one visual bump indicator that is visible before driving over the at least one bump. ### Claim 3 (depends on 1) The method according to claim 1, comprising generating the at least one visual bump indicator, wherein the generating comprises: obtaining video information and telemetric information, wherein the video information and the telemetric information are obtained during driving sessions of one or more vehicles; determining, based at least one the telemetric information, multiple suspected driving over bump events; selecting video information segments, wherein each selected video information segment is acquired before a suspected driving over bump event and in timing proximity to the suspected driving over bump event; and applying a machine learning process on the selected video information segments to find the at least one visual bump indicator that is visible before driving over the at least one bump. ### Claim 4 (depends on 1) The method according to claim 1 comprising: obtaining selected video information segments, wherein each selected video information segment is acquired before a suspected driving over bump event and in timing proximity to the suspected driving over bump event; and processing at least the selected video information segments to find the at least one visual bump indicator that is visible before driving over at least one bump. ### Claim 5 (depends on 4) The method according to claim 4 wherein the processing comprises applying a machine learning process on the at least the selected video information segments. ### Claim 6 (depends on 4) The method according to claim 4 wherein a determining of a suspected driving over bump event comprising searching, in the telemetric information, for a mechanical indication that a front of a vehicle rose and then fell within a distance of less than a few a meters. ### Claim 7 (depends on 5) The method according to claim 5 comprising determining, based on telemetric information acquired before the suspected driving over bump events and in timing proximity to the suspected driving over bump events, a recommended driving behavior before reaching the suspected driving over bump events. ### Claim 8 (depends on 5) The method according to claim 5 comprising determining a recommended driving behavior before reaching the suspected driving over bump events, wherein the determining is based on (a) telemetric information acquired during suspected driving over bump events, and (b) telemetric information acquired before the suspected driving over bump events and in timing proximity to the suspected driving over bump events. ### Claim 9 (depends on 4) The method according to claim 4 wherein the applying of the machine learning process comprises classifying the bumps to bump classes. ### Claim 10 (depends on 8) The method according to claim 8 wherein the at least one visual bump indicator comprises one or more visual bump indicator per each of the bump classes. ### Claim 11 (depends on 8) The method according to claim 8 comprising determining, based on telemetric information acquired before one or more suspected driving over bump events associated with a given bump class, and in timing proximity to the suspected driving over bump events associated with the given bump class, a recommended driving behavior before reaching a bump of the given bump class. ### Claim 12 (depends on 8) The method according to claim 8 comprising generating a bump map, wherein the bump map associated to at least some bumps a bump class. ### Claim 13 (depends on 4) The method according to claim 4 comprising obtaining other video information segments; wherein the processing comprises processing the other video information segments and the selected video information segments to find the at least one visual bump indicator. ### Claim 14 (depends on 13) The method according to claim 13 wherein the processing comprises applying a machine learning process on the at least the selected video information segments. ### Claim 15 (depends on 13) The method according to claim 13 wherein the processing comprises: obtaining first occurrence information regarding occurrences of objects within the selected video information segments; obtaining second occurrence information regarding occurrences of objects within the other video information segments; and determining the at least one bump indicator, based on the first occurrence information and the second occurrence information. ### Claim 16 (depends on 1) The method according to claim 1 comprising generating an ADAS response when determining that the vehicle is approaches the bump. ### Claim 17 (depends on 12) The method according to claim 12 wherein the ADAS response comprises recommendation aimed to a human driver. ### Claim 18 (depends on 12) The method according to claim 12 wherein the ADAS response comprises controlling an aspect of driving a vehicle. ### Claim 19 A non-transitory computer readable medium that stores instructions for: receiving by a vehicle computerized system, at least one visual bump indicator that is visible before driving over at least one bump; obtaining sensed information regarding an environment of the vehicle; processing the sensed information, wherein the processing comprises searching a visual bump indicator of the at least one visual bump indicator; determining whether the vehicle approaches a bump; and generating a bump alert when determining that the vehicle approaches the bump.