Publicação
All-digital ADC for an automotive LiDAR receiver
| Resumo: | Every 23 seconds, one more person dies in a traffic accident, with this being the leading cause of death for children and young adults. The main reason for traffic accidents is the human factor, with about 90% of all road casualties being rooted in human error. Autonomous Driving (AD) is, therefore, highly suggested as one of the most promising solutions to this problem. The ultimate goal would be to have a fully autonomous vehicle, without the need of any human intervention. If the vehicle is to take over the steering wheel, it must be able to perform all the tasks that the human was performing before, with one of the critical ones being the ability to reliably perceive its surroundings. LiDAR sensors have proven to have a key role in achieving such requirements, as they are the only type of sensor able to provide a detailed point cloud of the vehicle’s surroundings. LiDARs can be divided in two categories: full-waveform and TDC-based LiDARs. Contrary to TDC-based LiDARs, in full-waveform LiDARs, the full received pulse is captured, which enables precise range estimation, and discrimination of interfering or ghost pulses. However, fast and accurate ADCs are required, which, if resorting to conventional ADCs may result in a prohibitive Size, Weight, Power and Cost (SWaP-C). In this work, an All-Digital Analog-to-Digital Converter (AD-ADC) is custom-designed to enable a precise reconstruction of the LiDAR waveforms, while keeping SWaP-C low. An ideal model of the proposed ADC was first obtained, with a final AD-ADC model being constructed from this first one, by successively adding and analysing new impairments, including realistic time resolution and reference curve. The influence of finite slew rate and timing jitter was also addressed. All ADC models were applied to an experimental dataset of LiDAR waveforms, followed by a pre-selected range estimation method. In the end, a centimeter precision and accuracy were obtained over the majority of the waveforms. The results also suggest that the estimation performance may be further improved, by overcoming the proposed AD-ADC inherent drawback of performing non-uniform sampling, and by optimizing the considered reference curve. In conclusion, this study demonstrates that the proposed digital ADC may configure an advantageous part of an automotive LiDAR, being a viable option for the automotive industry. |
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| Autores principais: | Cabete, Laura Figueiredo |
| Assunto: | Autonomous driving LiDAR LiDAR estimation methods A/D conversion |
| Ano: | 2022 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso embargado |
| Instituição associada: | Universidade de Aveiro |
| Idioma: | inglês |
| Origem: | RIA - Repositório Institucional da Universidade de Aveiro |
| Resumo: | Every 23 seconds, one more person dies in a traffic accident, with this being the leading cause of death for children and young adults. The main reason for traffic accidents is the human factor, with about 90% of all road casualties being rooted in human error. Autonomous Driving (AD) is, therefore, highly suggested as one of the most promising solutions to this problem. The ultimate goal would be to have a fully autonomous vehicle, without the need of any human intervention. If the vehicle is to take over the steering wheel, it must be able to perform all the tasks that the human was performing before, with one of the critical ones being the ability to reliably perceive its surroundings. LiDAR sensors have proven to have a key role in achieving such requirements, as they are the only type of sensor able to provide a detailed point cloud of the vehicle’s surroundings. LiDARs can be divided in two categories: full-waveform and TDC-based LiDARs. Contrary to TDC-based LiDARs, in full-waveform LiDARs, the full received pulse is captured, which enables precise range estimation, and discrimination of interfering or ghost pulses. However, fast and accurate ADCs are required, which, if resorting to conventional ADCs may result in a prohibitive Size, Weight, Power and Cost (SWaP-C). In this work, an All-Digital Analog-to-Digital Converter (AD-ADC) is custom-designed to enable a precise reconstruction of the LiDAR waveforms, while keeping SWaP-C low. An ideal model of the proposed ADC was first obtained, with a final AD-ADC model being constructed from this first one, by successively adding and analysing new impairments, including realistic time resolution and reference curve. The influence of finite slew rate and timing jitter was also addressed. All ADC models were applied to an experimental dataset of LiDAR waveforms, followed by a pre-selected range estimation method. In the end, a centimeter precision and accuracy were obtained over the majority of the waveforms. The results also suggest that the estimation performance may be further improved, by overcoming the proposed AD-ADC inherent drawback of performing non-uniform sampling, and by optimizing the considered reference curve. In conclusion, this study demonstrates that the proposed digital ADC may configure an advantageous part of an automotive LiDAR, being a viable option for the automotive industry. |
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