Vehicle Speed Profile Control With Jerk-Limited Event Anticipation
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Solution Overview
Problem
Existing adaptive cruise control systems for motor vehicles do not adequately account for contextual and semantic information from the road scene, leading to suboptimal vehicle behavior and energy inefficiency in speed control.
Innovation Solution
A method for determining a speed profile that incorporates contextual information from a multi-sensor system, dividing the speed adjustment into three phases with controlled jerk and acceleration to optimize vehicle dynamics and energy efficiency, using bisection to calculate optimal acceleration values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If adaptive cruise control systems control vehicle speed based on basic safety distance and instruction speed, then the vehicle can maintain safe following distance and reach target speed, but the vehicle behavior is not adapted to contextual road environment information leading to suboptimal energy efficiency
Solution Approach 1:
The system performs preliminary detection of road context events (such as intersections, pedestrian crossings, speed limit changes) using multi-sensor data before the vehicle reaches them. Based on this advance detection, the control system pre-calculates and applies optimized speed profiles that anticipate upcoming events, allowing the vehicle to adjust speed proactively rather than reactively, thereby improving energy efficiency while maintaining adaptability to the road environment
Solution Approach 2:
The control system dynamically adjusts the speed profile by continuously integrating real-time sensor data about road context events with the vehicle's current state. The system modifies acceleration and deceleration patterns based on detected events such as intersections or pedestrian crossings, creating adaptive speed trajectories that optimize energy consumption while responding to changing environmental conditions
2Adaptability or versatility
If the system incorporates contextual information from multi-sensor systems to anticipate road events, then vehicle behavior becomes more adapted to the environment, but the device complexity increases
Solution Approach 1:
The control system is designed to handle multiple types of road context events (intersections, pedestrian crossings, speed limit changes, bends) using a unified event detection and response framework. The same control architecture processes different event types by recognizing patterns in multi-sensor data, allowing the system to maintain high adaptability across diverse scenarios without proportionally increasing complexity for each specific event type
Solution Approach 2:
The system introduces an intermediate event detection and processing layer that sits between the multi-sensor detection system and the speed control execution. This intermediary layer abstracts complex sensor data into standardized event representations, simplifying the control logic while maintaining comprehensive environmental awareness. The intermediary processor translates raw sensor inputs into actionable event signals that the control system can handle uniformly
3Loss of energy
If the system uses complex speed profile calculations to optimize energy efficiency, then energy consumption is reduced, but the computational complexity and calibration requirements increase
Solution Approach 1:
The system optimizes energy efficiency by dynamically adjusting key speed profile parameters (acceleration rates, deceleration rates, target speeds at specific points) based on detected road context events. Rather than using complex algorithms, the system modifies these parameters within predefined ranges to create energy-optimal speed trajectories. For example, when an intersection is detected, the system adjusts the deceleration rate and timing parameters to minimize energy consumption while ensuring safe stopping, thereby achieving energy efficiency through parameter optimization rather than computational complexity
Data Source
AI summary
A method for determining a speed profile to be followed by a vehicle, including acquiring event data including a distance from an event and a target speed at this event for the vehicle, and determining a speed profile to be followed as a function of time, between an initial speed and the target speed in three successive distinct phases, respectively a first phase in which the jerk is set constant at a predetermined maximum jerk value to reach an optimal target acceleration value, a second phase in which the optimal target acceleration value is kept constant, and a third phase in which the jerk is again set constant to reach a zero acceleration value at the end of the third phase. The optimal target acceleration value is such that the distance required to carry out the three phases of the profile is equal to the distance from the event.