A multi layer learning based control system and method for an autonomous vehicle or mobile robot. A mission planning layer, behavior planning layer and motion planning layer each having one or more neural neworks are used to develop an optimal route for the autonomous vehicle or mobile robot, provide a series of functional tasks associated with at least one or more of the neural networks to follow the planned optimal route and develop commands to implement the functional tasks.

A method for determining the start of movement of a motor vehicle equipped with a system for monitoring tire pressure of a motor vehicle. Communication between the receiver and each pressure monitoring system emitter being subjected to a Doppler effect so that a periodic component is inserted by the emitter into the signal emitted to the receiver. The method includes: acquiring the intermediate-frequency signal before demodulation by a processor to extract data carried by the radiofrequency signal, determining the FFT of the IF signal, determining the average value of the FFT of the IF signal over a preset duration, and determining whether there is a frequency deviation by comparing the instantaneous value of the fast Fourier transform to the average value of the fast Fourier transform, if so, determining whether the emitter for monitoring the pressure of a tire is moving and that the vehicle is moving.

The present invention provides a method and an apparatus for dynamically controlling the luminance of a tail light of a vehicle tail lamp in response to a change in the difference of speed between a vehicle and a vehicle therebehind, and for preventing a collision between the traveling vehicles. In addition, the apparatus comprises: a tail light operating unit, which slowly or rapidly becomes brighter up to a predetermined brightness as the speed difference between the vehicle and the vehicle behind gradually becomes larger, and slowly or rapidly becomes darker down to a predetermined brightness as the speed difference between the vehicle and the vehicle behind gradually becomes smaller; a speed sensing unit for measuring the speed of the vehicle; a rear speed sensing unit for measuring the speed of the vehicle therebehind; and a control unit for automatically adjusting the luminance of the tail light by controlling the tail light operating unit when the vehicle therebehind is traveling faster than the vehicle.

An improved method for locating an acoustic source relative to a vehicle that requires, for example, only a single microphone is disclosed. The method comprises: obtaining an acoustic signal transmitted by the acoustic source; determining an observer frequency, referenced to the vehicle, of the acoustic signal; stipulating a velocity of the acoustic source; stipulating a relative position of the acoustic source relative to a position of the vehicle; determining a signal frequency; and locating the acoustic source by performing, n times, a Doppler calculation using the determined observer frequency, the stipulated velocity, the determined signal frequency, and the stipulated relative position.

A system and method to avoid collisions on highways, and to minimize the fatalities, injury, and damage when a collision is unavoidable. The system includes sensor means to detect other vehicles, and computing means to evaluate when a collision is imminent and to determine whether the collision is avoidable. If the collision is avoidable by a sequence of controlled accelerations and decelerations and steering, the system implements that sequence of actions automatically. If the collision is unavoidable, a different sequence is implemented to minimize the overall harm of the unavoidable collision. The system further includes indirect mitigation steps such as flashing the brake lights automatically. An optional post-collision strategy is implemented to prevent secondary collisions, particularly if the driver is incapacitated. Adjustment means enable the driver to set the type and timing of automatic interventions.

A vehicle includes a set of sound sensors coupled to one or more processing systems that process sound data from the set of sound sensors in order to provide assisted driving features or functionality such as an emergency vehicle avoidance.

A method controls an operational mode of a self-driving vehicle (SDV). One or more physical detectors detect an erratically driven vehicle (EDV) that is being operated in an unsafe manner within a predetermined distance of an SDV that is initially being operated in an evasive autonomous mode. One or more processors retrieve traffic pattern data for other SDVs, and examine the traffic pattern data to 1) determine a first traffic flow of the other SDVs while operating in the evasive autonomous mode, and 2) determine a second traffic flow of the other SDVs while operating in a manual mode. In response to determining that the first traffic flow has a higher accident rate than the second traffic flow, an operational mode device changes the operational mode of the SDV from the evasive autonomous mode to the manual mode.

A system and method to avoid collisions on highways, and to minimize the fatalities, injury, and damage when a collision is unavoidable. The system includes sensor means to detect other vehicles, and computing means to evaluate when a collision is imminent and to determine whether the collision is avoidable. If the collision is avoidable by a sequence of controlled accelerations and decelerations and steering, the system implements that sequence of actions automatically. If the collision is unavoidable, a different sequence is implemented to minimize the overall harm of the unavoidable collision. The system further includes indirect mitigation steps such as flashing the brake lights automatically. An optional post-collision strategy is implemented to prevent secondary collisions, particularly if the driver is incapacitated. Adjustment means enable the driver to set the type and timing of automatic interventions.

Disclosed are systems and methods for autonomous vehicles and vehicles with automatic driver-assistance systems (ADAS) to automatically detect an imminent collision, determine whether the collision is avoidable or unavoidable, and plot a course minimizing the hazard using an artificial intelligence (AI) model. For example, a collision is avoidable if the vehicle can avoid it by steering, braking, and/or accelerating in a particular sequence. The AI model finds the best sequence for collision avoidance, and if that is not possible, it finds the best sequence for minimizing the harm. The harm is based on an estimated number of fatalities, injuries, and property damage predicted to be caused in the collision. The AI-based situation analysis and sequence selection are directly applicable to human-driven vehicles with an emergency-intervention ADAS system, as well as fully autonomous vehicles. With fast electronic reflexes and multi-sensor situation awareness, the AI model can save lives on the highway.

Most automatic driver-assistance systems, including allegedly autonomous driving systems, fail to react to certain hazard cues that human drivers instinctively notice. Chief among those cues is the sudden illumination of the brake lights in the vehicle ahead. Admittedly, it is difficult for software to discriminate between brake lights, turn signal lights, and running lightsbut that is what safety requires. Disclosed herein are methods and systems for processors on vehicles to interpret sensor images, detect brake lights ahead, and take proper avoidance action such as braking and swerving. The proper strategy may be decided according to the relative speed of the two vehicles and their distance apart when the brake lights come on, among other factors. With such foresighted collision mitigation capability, autonomous and semi-autonomous vehicles may be able to save many lives due to unnecessary collisions in traffic.