Walking into artificial intelligence | Autonomous driving opens a new era of intelligent travel

foreword

Autonomous driving, also known as driverless or self-driving cars, refers to technologies and systems that can autonomously perceive the environment, make decisions, and control vehicle driving without human intervention.

theme

A self-driving car is a smart car that realizes unmanned driving through a computer system. The vehicle relies on artificial intelligence, visual computing, radar, surveillance devices, and global positioning systems to work together to allow computers to automatically and safely operate motor vehicles without any active human intervention.

Self-driving technology is one of the most high-profile innovations in technology today. With the rapid development of artificial intelligence and sensor technology, driverless cars are gradually becoming a reality. This technology has huge potential to revolutionize the way we travel, improving safety and efficiency.

The core concept of autonomous driving technology is to improve the efficiency and safety of the transportation system by reducing or eliminating the intervention of human factors. It can reduce traffic accidents, improve road utilization and traffic efficiency, while promoting clean and sustainable cities.

This article will discuss the development trend, application fields and challenges of autonomous driving technology.

development trend

The development of autonomous driving technology has gone through several stages. The initial stages were assisted driving, over time the technology has gradually evolved to partially autonomous driving, and today we are moving towards the goal of fully autonomous driving, that is, a fully autonomous driving system without human intervention.

Image source: Qianzhan Industry Research Institute

Assisted driving stage (1990s to present): This stage of autonomous driving technology aims to assist the driver and provide some assistance functions, such as adaptive cruise control (ACC) and lane keeping assist system (LKAS). These systems use sensors and algorithms to monitor and control the vehicle's speed, distance, and lane position, alleviating the burden on the driver.

Partially autonomous driving stage (2000s to present): In this stage, self-driving technology moves towards higher levels of automation. Partially autonomous cars can drive autonomously under certain conditions, such as on marked roads on highways. Drivers still need to keep monitoring the vehicle, but they can transfer control of the vehicle to the automatic driving system.

Fully autonomous driving stage (2010s to present): This is the highest stage in the development of autonomous vehicles. Fully autonomous vehicles (also known as Level 5 autonomous vehicles) can autonomously drive anywhere and under any conditions without human intervention. This requires the use of advanced sensor technology, high-precision maps, artificial intelligence algorithms and powerful computing power to perceive and respond to complex traffic environments.

Autopilot level

Regarding the classification of automobile intelligence, there are two sets of standards in the industry. The first is formulated by NHSTA (National Highway Safety Administration) under the US Department of Transportation; the second is formulated by SAE International, the International Society of Automotive Engineers.

The standards of both organizations divide autonomous driving into L0~L5, where L0 refers to manual driving.

Standards of SAE International:

L0 level automatic driving

Regardless of driving operations, surrounding supervision, takeover, etc., the driver needs to complete it by himself, but this does not mean that L0 has no assistance. L0-level automatic driving can provide warnings and instant assistance, such as active braking, blind spot monitoring, body stabilization systems, and more.

L1 level automatic driving

L1 level automatic driving is that the vehicle and the driver jointly complete some driving operations, while the monitoring and taking over of the vehicle's surroundings are all completed by the driver himself, which belongs to the assisted driving level. For example, adaptive cruise control and lane departure warning belong to the L1 level. In fact, strictly speaking, the L2-level autonomous driving promoted by many manufacturers currently belongs to the L1 or L1.5 level.

L2 level automatic driving

The vehicle performs the operation, and the driver is responsible for surrounding monitoring and taking over at any time.

L2 level automatic driving is partly automatic driving. The basic operation is completed by the vehicle, and the driver is responsible for monitoring the surroundings and taking over the vehicle at any time. It mainly includes functions such as ACC automatic cruise, automatic car following, automatic parking and so on. To give an example of L2 level automatic driving, for example, we use automatic parking. When the vehicle automatically drives into the parking space, it is found that there are idle people intruding around the vehicle. At this time, the driver needs to intervene immediately.

Another example is when we use ACC adaptive cruise control, the vehicle in front decelerates rapidly or brakes suddenly. If such an unexpected situation occurs, we must manually intervene and respond immediately. Therefore, the ACC equipped on most vehicles has a certain speed range. limit. For vehicles equipped with full-speed adaptive cruise control (with higher automatic processing capabilities), it can also be called L2.5 level automatic driving.

L3 level automatic driving

L3 level automatic driving, in a specific environment, the vehicle can complete driving operations, surrounding monitoring and other actions, without the driver's own operation, but during the automatic driving process of the vehicle, the driver must be ready to take over the vehicle at any time. As for when to take over the vehicle, the system will automatically prompt the driver.

L4 level automatic driving

L4-level autonomous driving is more powerful than L3. At L4 level, the vehicle can complete driving operations, peripheral monitoring, and autonomous takeover responses without the need for the driver to take over the steering wheel. However, L4-level automatic driving has some restrictions, such as speed limits, driving area restrictions, and so on. In fact, for vehicles with relatively fixed driving routes and low speed requirements, L4 level automatic driving can be regarded as complete automatic driving.

L5 level automatic driving

L5 level self-driving vehicles are completely self-driving, and we don’t need to do any operation by ourselves. Rather than saying that we are driving a car, it is better to say that we are acting as passengers. Eat, sleep, surf the Internet, read, entertain, and do whatever you want in the car, with a high degree of freedom. This is also the most advanced state in the current autonomous driving concept.

summary

L0-L2 is driving assistance, and L4-L5 is automatic driving. It can be seen from the automatic driving level division standard that L0, L1 and L2 level automatic driving requires the driver to monitor the surrounding situation at all times, and the driver must complete most of the operations, so automatic driving cannot be achieved. At L3, L4 and L5 levels of autonomous driving, the surrounding environment supervision and driving operations are all completed by the vehicle itself. In contrast, L0, L1 and L2 levels of automatic driving are more appropriate to be called driving assistance.

Application field

Autonomous driving technology can be used in a wide range of fields, including private cars, public transport, freight and logistics.

In the field of personal vehicles, self-driving technology can greatly improve road safety. Statistics show that the vast majority of traffic accidents are caused by human driving errors. The revolution of the self-driving system is that it can avoid these mistakes and reduce the incidence of traffic accidents through real-time sensor data and advanced algorithms.

In the field of public transportation, autonomous driving technology can bring more efficient and reliable services. Driverless buses and taxis can accurately follow predetermined routes, reducing traffic jams and improving efficiency in picking up and dropping off passengers. In addition, autonomous driving technology can improve urban traffic planning, optimize traffic flow, and reduce environmental impact.

In the field of freight and logistics, autonomous driving technology also has great potential. Traditionally, the transportation of goods requires a lot of manpower and time costs. And unmanned trucks can realize the fast, accurate and safe transportation of goods through automated operation. This can not only improve the efficiency of logistics operations, but also reduce costs and reduce the interference of human factors on cargo transportation.

Core Technology

Autonomous driving mainly includes technologies such as sensor technology, computing power and artificial intelligence algorithms, high-precision maps, communication technology, safety systems, and error correction capabilities. These core technologies cooperate with each other to realize the environmental perception, decision-making and control of autonomous vehicles.

Sensor Technology

Self-driving cars use a variety of sensors to perceive their surroundings, including Lidar, cameras, radar, and ultrasonic sensors. These sensors can acquire information around the vehicle in real time, such as obstacles, road signs, traffic signals, etc.

Let's feel the code of the sensor

lidar sensor

import rospy
from sensor_msgs.msg import LaserScan

def callback(data):
    # 在这里处理激光雷达数据
    ranges = data.ranges
    # 其他处理逻辑...

rospy.init_node('lidar_listener', anonymous=True)
rospy.Subscriber('/scan', LaserScan, callback)
rospy.spin()

camera sensor

import cv2

def process_image(image):
    # 在这里处理摄像头图像
    # 可以使用OpenCV和图像处理算法进行处理
    # 其他处理逻辑...

cap = cv2.VideoCapture(0)
while True:
    ret, frame = cap.read()
    if ret:
        process_image(frame)
    else:
        break

cap.release()
cv2.destroyAllWindows()

ultrasonic sensor

import RPi.GPIO as GPIO
import time

GPIO.setmode(GPIO.BCM)

TRIG_PIN = 23
ECHO_PIN = 24

GPIO.setup(TRIG_PIN, GPIO.OUT)
GPIO.setup(ECHO_PIN, GPIO.IN)

def measure_distance():
    GPIO.output(TRIG_PIN, True)
    time.sleep(0.00001)
    GPIO.output(TRIG_PIN, False)

    while GPIO.input(ECHO_PIN) == 0:
        pulse_start = time.time()

    while GPIO.input(ECHO_PIN) == 1:
        pulse_end = time.time()

    pulse_duration = pulse_end - pulse_start
    distance = pulse_duration * 17150
    distance = round(distance, 2)

    return distance

try:
    while True:
        distance = measure_distance()
        # 在这里处理超声波传感器数据
        # 其他处理逻辑...

except KeyboardInterrupt:
    GPIO.cleanup()

Computing Power and AI Algorithms

Self-driving cars require powerful computing power and efficient artificial intelligence algorithms to process complex sensor data and make decisions. Deep learning and machine learning algorithms are widely used in autonomous driving systems for key tasks such as object detection, visual recognition, path planning, and behavior prediction.

HD map

Self-driving cars use high-precision maps for precise positioning and navigation. These maps contain detailed road information, traffic signs, lane lines, etc., to help vehicles better understand and plan routes.

communication technology

Autonomous vehicles communicate in real time with other vehicles, infrastructure, and central control systems to obtain real-time information about the surrounding environment, coordinate traffic, and receive instructions. Communication between vehicles can improve driving safety and efficiency.

Safety systems and error correction capabilities

Self-driving cars need to have strong safety systems and error correction capabilities to deal with emergencies and failures. For example, redundant sensors, backup computing units, and emergency braking systems are used to improve vehicle safety.

face the challenge

While self-driving technology holds great promise, it still faces some challenges. One of the main challenges is ensuring the security of the system. Autonomous driving systems require highly accurate sensor data and complex algorithms to make decisions. At the same time, a comprehensive test and verification of the system is required to ensure its reliability under various conditions.

In addition, legal and ethical issues are also an important consideration in the development of autonomous driving technology. For example, when an autonomous vehicle encounters an emergency, should it prioritize protecting passengers or pedestrians? These issues need to be carefully weighed and resolved to ensure the legality and ethics of self-driving technology.

Summarize

In conclusion, self-driving technology is moving towards a driverless future. It will revolutionize the way we travel and lead to a safer, more efficient and environmentally friendly transportation system. Although there are still some challenges, through the efforts of technological innovation, regulation improvement and social consensus, we are confident to overcome these challenges and realize the wide application of autonomous driving technology, so that we can move towards a more intelligent and convenient travel era.