An automatic shutoff system for a motor vehicle, the automatic shutoff system including a front suspension system disposed at a front portion of the motor vehicle, the front suspension system comprising at least one spring wrapped around at least one arm, at least one sensor disposed on the at least one arm to sense when the at least one spring has been jarred at a predetermined intensity, a curb jump detection system to receive at least one signal from the at least one sensor when the at least one spring has been jarred at the predetermined intensity, and to process the at least one signal to determine whether a curb has been jumped by the front suspension system, and a computer system to receive a signal from the curb jump detection system when the curb jump detection system determines that the curb has been jumped by the front suspension system, and to control a motor to shut off.

This disclosure relates to method and system for validating an Autonomous Vehicle (AV) stack. The method may include receiving an Operational Design Domain (ODD) and real-world data for evaluating at least one of an Advanced Driver Assistance System (ADAS) and the AV. The ODD is based on at least one feature of at least one of the ADAS and the AV. For each of a plurality of iterations, the method may further include generating a driving scenario based on the ODD of the AV and the real-world data through a Quality of Ride Experience (QoRE)-aware cognitive engine, plugging and running at least one of the ADAS and the AV algorithm based on the driving scenario, and determining a set of performance metrics corresponding to the at least one feature of at least one of the ADAS and the AV in the driving scenario based on the simulating.

The invention is a method and a system for autonomous motion control and motion planning of a vehicle, wherein the vehicle is defined as a hybrid system having hybrid modes. The method comprises the steps of calculating, by a motion control unit (20), a set of feasible vehicle states for each hybrid mode, calculating, by the motion control unit (20), a set of reachable vehicle states, wherein each of the reachable vehicle states is a feasible vehicle state that can be reached from a same feasible vehicle state or from an other feasible vehicle state, constructing a vehicle trajectory, by a motion planner unit (22), the vehicle trajectory being a sequence of consecutive feasible vehicle states, wherein the consecutive feasible vehicle states of the vehicle trajectory are reachable vehicle states on the basis of a previous feasible vehicle state, and controlling, by the motion control unit (20), at least one vehicle actuator to drive the vehicle to each of the consecutive feasible vehicle states of the vehicle trajectory.

The present technology is directed to dynamically adjusting an autonomous vehicle (AV) system based on deep learning optimizations. An AV management system can generate a downscaling signal based on a result of comparing a complexity of an environment for an AV to navigate with a predetermined complexity threshold. Further, the AV management system can perform a downscaling of a neural network associated with an AV system based on the downscaling signal and determine a scenario to test the downscaled neural network in a simulation. The AV management system can adjust one or more parameters of the AV system based on simulated outputs and perform the simulation of the AV based on the adjusted one or more parameters of the AV system and the downscaled neural network to generate simulated performance data. Furthermore, the AV management system can compare the simulated performance data with a predetermined performance threshold.

A control unit for a vehicle is designed to detect movement sensor data with regard to a movement of at least one component of the vehicle. The movement is or has been effected by a roadway driven upon by the vehicle. In addition, the control unit is designed to detect a lane boundary of the roadway on the basis of movement sensor data and, in reaction thereto, to cause a functional reaction of the vehicle.

Methods and systems for reducing vehicle and animal collisions. One system includes an electronic processor configured to receive vehicle data. The electronic processor is also configured to determine a collision risk of the vehicle based on the vehicle data, the collision risk representing a probability of a collision between the vehicle and an animal. The electronic processor is also configured to adjust a collision parameter of the vehicle based on the collision risk. The electronic processor is also configured to identify when an animal is in a path of the vehicle based on the vehicle data. The electronic processor is also configured to, when an animal is identified in the path of the vehicle, automatically perform a vehicle operation based on the adjusted collision parameter.

In various examples, the ride quality of an autonomous vehicle is improved by selecting a lateral lane position based at least in part on measured road-condition information. In various examples, information describing the condition of a road is obtained by measuring vehicle motion and road noise using sensors on the vehicle. This information is stored in association with the lateral lane position of the vehicle to produce a profile describing expected ride quality as a function of lateral lane position. In some examples, this information is uploaded and aggregated on a remote server to produce a lateral profile of road condition using information collected by many vehicles.

The present disclosure provides a vehicle collision mitigation apparatus and method, in which it is determined whether or not a collision will occur between a target vehicle and a lateral side of a host vehicle based on the paths of the host vehicle and the target vehicle, and if a collision is unavoidable, a traveling state, including a vehicle speed, steering, a suspension, or a position of a seat, is changed such that the impact caused by the collision is reduced. According to the present disclosure, it is possible to minimize damage due to the collision of the target vehicle with the lateral side of the host vehicle.

Methods and apparatus to compensate for body roll in vehicle weight calculations are disclosed. An example method includes receiving sensor data from sensors of a vehicle, determining a weight of the vehicle and determining a body roll of the vehicle. The example method further includes comparing the body roll to a threshold and, if the body roll satisfies the threshold, adjusting the determined weight of the vehicle based on the determined body roll and properties of a suspension system of the vehicle.

Disclosed are an apparatus for estimating a bounce speed of a vehicle and a method thereof. The apparatus includes an acceleration sensor that detects an acceleration of the vehicle, a front wheel speed sensor that detects a wheel speed of a front wheel of the vehicle, a rear wheel speed sensor that detects a wheel speed of a rear wheel of the vehicle, and a controller. The controller determines a wheel acceleration of the front wheel based on the wheel speed of the front wheel, determines a wheel acceleration of the rear wheel based on the wheel speed of the rear wheel, and estimates the bounce speed of the vehicle. The bounce speed of the vehicle is estimated based on the acceleration of the vehicle, the wheel acceleration of the front wheel, and the wheel acceleration of the rear wheel.